Bibliografías temáticas / Deep Learning, Computer Vision, Object Detection

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Literatura académica sobre el tema "Deep Learning, Computer Vision, Object Detection"

Autor: Grafiati

Publicado: 10 de marzo de 2023

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Artículos de revistas sobre el tema "Deep Learning, Computer Vision, Object Detection"

Poojitha, L. "Anomalous Object Detection with Deep Learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 6 (30 de junio de 2022): 3227–32. http://dx.doi.org/10.22214/ijraset.2022.44581.

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Abstract: In many computers vision systems, object identification and monitoring are crucial characteristics. Object identification and monitoring is a difficult job in the fields of computer vision that attempts to identify, recognize and track things across a video series of pictures. It aids in the understanding and description of object behaviour rather than relying on human operators to monitor the computers. Its goal is to find moving things in a video clip or a security camera. On the other hand, rely heavily on computers, on the other hand many respect attributes approaches. The system collects a snapshot from the camera, process it as per the models requirements and the outputs the data to the tensorflow framework which provides a list of the frames discoveries and the objects dependability points and connections to their binding containers. Before exposing the object to the user, the software takes those connections and creates a rectangle adjacent to it. This research detects the presence of anomalous items in camera-captured sequences, with anomalies being things that correspond to categories that should not be present in a given scene.

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Singh, Baljeet, Nitin Kumar, Irshad Ahmed y Karun Yadav. "Real-Time Object Detection Using Deep Learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 5 (31 de mayo de 2022): 3159–60. http://dx.doi.org/10.22214/ijraset.2022.42820.

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Abstract: The computer vision field known as real-time acquisition is large, dynamic, and complex. Local image process refers to the acquisition of one object in an image, while Objects refers to the acquisition of multiple objects in an image. In digital photos and videos, this sees semantic class objects. Tracking features, video surveilance, pedestrian detection, census, self-driving cars, face recognition, sports tracking, and many other applications used to find real-time object. Convolution Neural Networks is an in-depth study tool for OpenCV (Opensource Computer Vision), a set of basic computer-assisted programming tasks. Computer visualization, in-depth study, and convolutional neural networks are some of the words used in this paper..

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Pernando, Yonky, Eka Lia Febrianti, Ilwan Syafrinal, Yuni Roza y Ummul Fitri Afifah. "DEEP LEARNING FOR FACES ON ORPHANAGE CHILDREN FACE DETECTION". JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 9, n.º 1 (16 de diciembre de 2022): 25–32. http://dx.doi.org/10.33330/jurteksi.v9i1.1858.

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Abstract: l -The field of computer vision is research in development technology to obtain information from images and replicate or imitate human visual processes, so that they can understand the objects around them. Deep learning is a term used to describe a new era in learning that supports computer learning from big data machines. Convolutional Neural Networks (CNN) algorithms have made significant progress in the fields of object detection, image classification, and semantic segmentation. ;Object detection is a technique used to identify the type of object in a given image and the location of the object in the image. The field of computer vision is research in development technology to obtain information from images and replicate or imitate human visual processes, so that computers can know objects around them. Deep learning is the buzzword as a new era in machine learning that trains computers to find patterns from large amounts of data. Convolutional Neural Networks (CNN) algorithms have made significant progress in the fields of object detection, image classification, and semantic segmentation. Object detection is a technique used to identify the type of object in a particular image as well as the location of the object in the image. Keywords: CNN, Computer Vision, Deep Learning, Face Detection; Abstrak: 1 Bidang computer vision merupakan penelitian dalam teknologi pembangunan untuk memperoleh informasi dari citra dan mereplikasi atau meniru proses visual manusia, sehingga dapat memahami objek - objek disekelilingnya. Pembelajaran mendalam adalah istilah yang digunakan untuk menggambarkan era baru dalam pembelajaran mesin yang memungkinkan komputer belajar dari sejumlah besar data. [Algoritma Convolutional Neural Networks (CNN) telah membuat kemajuan yang signifikan di bidang deteksi objek, klasifikasi gambar, dan segmentasi semantik. Deteksi objek adalah teknik yang digunakan untuk mengidentifikasi jenis objek dalam citra yang diberikan serta lokasi objek di dalam citra. Bidang computer vision merupakan penelitian dalam teknologi pembangunan untuk memperoleh informasi dari citra dan mereplikasi atau meniru proses visual manusia, sehingga komputer dapat mengetahui objek - objek disekelilingnya. Deep learning adalah kata kunci sebagai era baru dalam machine learning yang melatih komputer dalam menemukan pola dari jumlah besar data. Algoritma Convolutional Neural Networks (CNN) telah membuat kemajuan yang signifikan di bidang deteksi objek, klasifikasi gambar, dan segmentasi semantik. /Deteksi objek adalah teknik yang digunakan untuk mengidentifikasi jenis objek dalam citra tertentu serta lokasi objek di dalam citra. Kata kunci: CNN, Computer Vision, Deep Learning, Deteksi Wajah

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Singh, Ankita. "Face Mask Detection using Deep Learning to Manage Pandemic Guidelines". Journal of Management and Service Science (JMSS) 1, n.º 2 (2021): 1–21. http://dx.doi.org/10.54060/jmss/001.02.003.

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The field of Computer Vision is a branch of science of the computers and systems of software in which one can visualize and as well as comprehend the images and scenes given in the input. This field is consisting of numerous aspects for example image recognition, the detection of objects, generation of images, image super resolution and more others. Object detection is broadly utilized for the detection of faces, the detection of vehicles, counting of pedestrians on a certain street, images displayed on the web, security systems and cars with the feature of self-driving. This process also encompasses the precision of every technique for recognizing the objects. The detection of objects is a crucial task; however, it is also a very challenging vision task. It is an analytical subdivide of various applications such as searching of images, image auto-annotation or scene understanding and tracking of various objects. The tracking of objects in motion of video image sequence was one of the most important subjects in computer vision.

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Zhu, Juncai, Zhizhong Wang, Songwei Wang y Shuli Chen. "Moving Object Detection Based on Background Compensation and Deep Learning". Symmetry 12, n.º 12 (27 de noviembre de 2020): 1965. http://dx.doi.org/10.3390/sym12121965.

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Detecting moving objects in a video sequence is an important problem in many vision-based applications. In particular, detecting moving objects when the camera is moving is a difficult problem. In this study, we propose a symmetric method for detecting moving objects in the presence of a dynamic background. First, a background compensation method is used to detect the proposed region of motion. Next, in order to accurately locate the moving objects, we propose a convolutional neural network-based method called YOLOv3-SOD for detecting all objects in the image, which is lightweight and specifically designed for small objects. Finally, the moving objects are determined by fusing the results obtained by motion detection and object detection. Missed detections are recalled according to the temporal and spatial information in adjacent frames. A dataset is not currently available specifically for moving object detection and recognition, and thus, we have released the MDR105 dataset comprising three classes with 105 videos. Our experiments demonstrated that the proposed algorithm can accurately detect moving objects in various scenarios with good overall performance.

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Taralathasri, Bobburi, Dammati Vidya Sri, Gadidammalla Narendra Kumar, Annam Subbarao y Palli R. Krishna Prasad. "REAL TIME OBJECT DETECTION USING YOLO ALGORITHM". International Journal of Computer Science and Mobile Computing 10, n.º 7 (30 de julio de 2021): 61–67. http://dx.doi.org/10.47760/ijcsmc.2021.v10i07.009.

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The major and wide range applications like Driverless cars, robots, Image surveillance has become famous in the Computer vision .Computer vision is the core in all those applications which is responsible for the image detection and it became more popular worldwide. Object Detection System using Deep Learning Technique” detects objects efficiently based on YOLO algorithm and applies the algorithm on image data to detect objects.

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Jyothi, Madapati Asha y Mr M. Kalidas. "Real Time Smart Object Detection using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 11 (30 de noviembre de 2022): 212–17. http://dx.doi.org/10.22214/ijraset.2022.47281.

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Abstract: Efficient and accurate object detection has been an important topic in the advancement of computer vision systems. With the advent of deep learning techniques, the accuracy for object detection has increased drastically. The project aims to incorporate state-of-the-art technique for object detection with the goal of achieving high accuracy with a real-time performance. A major challenge in many of the object detection systems is the dependency on other computer vision techniques for helping the deep learning based approach, which leads to slow and non-optimal performance. In this project, we use a completely deep learning based approach to solve the problem of object detection in an end-to-end fashion. The network is trained on the most challenging publicly available data-set, on which a object detection challenge is conducted annually. The resulting system is fast and accurate, thus aiding those applications which require object detection

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Kumar, Aayush, Amit Kumar, Avanish Chandra y Indira Adak. "Custom Object Detection and Analysis in Real Time: YOLOv4". International Journal for Research in Applied Science and Engineering Technology 10, n.º 5 (31 de mayo de 2022): 3982–90. http://dx.doi.org/10.22214/ijraset.2022.43303.

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Abstract: Object recognition is one of the most basic and complex problems in computer vision, which seeks to locate object instances from the enormous categories of already defined and readily available natural images. The object detection method aims to recognize all the objects or entities in the given picture and determine the categories and position information to achieve machine vision understanding. Several tactics have been put forward to solve this problem, which is more or less inspired by the principles based on Open Source Computer Vision Library (OpenCV) and Deep Learning. Some are relatively good, while others fail to detect objects with random geometric transformations. This paper proposes demonstrating the " HAWKEYE " application, a small initiative to build an application working on the principle of EEE i.e. (Explore→Experience→Evolve). Keywords: Convolution Neural Network, Object detection, Image classification, Deep learning, Open CV, Yolov4.

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Saiful, Muhammad, Lalu Muhammad Samsu y Fathurrahman Fathurrahman. "Sistem Deteksi Infeksi COVID-19 Pada Hasil X-Ray Rontgen menggunakan Algoritma Convolutional Neural Network (CNN)". Infotek : Jurnal Informatika dan Teknologi 4, n.º 2 (31 de julio de 2021): 217–27. http://dx.doi.org/10.29408/jit.v4i2.3582.

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The development of the world's technology is growing rapidly, especially in the field of health in the form of detection tools of various objects, including disease objects. The technology in point is part of artificial intelligence that is able to recognize a set of imagery and classify automatically with deep learning techniques. One of the deep learning networks widely used is convolutional neural network with computer vision technology. One of the problems with computer vision that is still developing is object detection as a useful technology to recognize objects in the image as if humans knew the object of the image. In this case, a computer machine is trained in learning using artificial neural networks. One of the sub types of artificial neural networks that are able to handle computer vision problems is by using deep learning techniques with convolutional neural network algorithms. The purpose of this research is to find out how to design the system, the network architecture used for COVID-19 infection detection. The system cannot perform detection of other objects. The results of COVID-19 infection detection with convolutional neural network algorithm show unlimited accuracy value that ranges from 60-99%.

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Kumar, Chandan. "Hill Climb Game Play with Webcam Using OpenCV". International Journal for Research in Applied Science and Engineering Technology 10, n.º 12 (31 de enero de 2022): 441–53. http://dx.doi.org/10.22214/ijraset.2022.39860.

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Abstract: Computer vision is a process by which we can understand how the images and videos are stored and manipulated, also it helps in the process of retrieving data from either images or videos. Computer Vision is part of Artificial Intelligence. Computer-Vision plays a major role in Autonomous cars, Object detections, robotics, object tracking, etc. OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. It comes with a highly improved deep learning (dnn ) module. This module now supports a number of deep learning frameworks, including Caffe, TensorFlow, and Torch/PyTorch. This does allow us to take our models trained using dedicated deep learning libraries/tools and then efficiently use them directly inside our OpenCV scripts. MediaPipe is a framework mainly used for building audio, video, or any time series data. With the help of the MediaPipe framework, we can build very impressive pipelines for different media processing functions like Multi-hand Tracking, Face Detection, Object Detection and Tracking, etc.

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Tesis sobre el tema "Deep Learning, Computer Vision, Object Detection"

Kohmann, Erich. "Tecniche di deep learning per l'object detection". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19637/.

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L’object detection è uno dei principali problemi nell’ambito della computer vision. Negli ultimi anni, con l’avvento delle reti neurali e del deep learning, sono stati fatti notevoli progressi nei metodi per affrontare questo problema. Questa tesi intende fornire una rassegna dei principali modelli di object detection basati su deep learning, di cui si illustrano le caratteristiche fondamentali e gli elementi che li contraddistinguono dai modelli precedenti. Dopo un infarinatura iniziale sul deep learning e sulle reti neurali in genere, vengono presentati i modelli caratterizzati da tecniche innovative che hanno portato ad un miglioramento significativo, sia nella precisione e nell’accuratezza delle predizioni, che in termini di consumo di risorse. Nella seconda parte l’elaborato si concentra su YOLO e sui suoi sviluppi. YOLO è un modello basato su reti neurali convoluzionali, con il quale i problemi di localizzazione e classificazione degli oggetti in un’immagine sono stati trattati per la prima volta come un unico problema di regressione. Questo cambio di prospettiva apportato dagli autori di YOLO ha aperto la strada verso un nuovo approccio all’object detection, facilitando il successivo sviluppo di modelli sempre più precisi e performanti.

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Andersson, Dickfors Robin y Nick Grannas. "OBJECT DETECTION USING DEEP LEARNING ON METAL CHIPS IN MANUFACTURING". Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55068.

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Designing cutting tools for the turning industry, providing optimal cutting parameters is of importance for both the client, and for the company's own research. By examining the metal chips that form in the turning process, operators can recommend optimal cutting parameters. Instead of doing manual classification of metal chips that come from the turning process, an automated approach of detecting chips and classification is preferred. This thesis aims to evaluate if such an approach is possible using either a Convolutional Neural Network (CNN) or a CNN feature extraction coupled with machine learning (ML). The thesis started with a research phase where we reviewed existing state of the art CNNs, image processing and ML algorithms. From the research, we implemented our own object detection algorithm, and we chose to implement two CNNs, AlexNet and VGG16. A third CNN was designed and implemented with our specific task in mind. The three models were tested against each other, both as standalone image classifiers and as a feature extractor coupled with a ML algorithm. Because the chips were inside a machine, different angles and light setup had to be tested to evaluate which setup provided the optimal image for classification. A top view of the cutting area was found to be the optimal angle with light focused on both below the cutting area, and in the chip disposal tray. The smaller proposed CNN with three convolutional layers, three pooling layers and two dense layers was found to rival both AlexNet and VGG16 in terms of both as a standalone classifier, and as a feature extractor. The proposed model was designed with a limited system in mind and is therefore more suited for those systems while still having a high accuracy. The classification accuracy of the proposed model as a standalone classifier was 92.03%. Compared to the state of the art classifier AlexNet which had an accuracy of 92.20%, and VGG16 which had an accuracy of 91.88%. When used as a feature extractor, all three models paired best with the Random Forest algorithm, but the accuracy between the feature extractors is not that significant. The proposed feature extractor combined with Random Forest had an accuracy of 82.56%, compared to AlexNet with an accuracy of 81.93%, and VGG16 with 79.14% accuracy.
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Arefiyan, Khalilabad Seyyed Mostafa. "Deep Learning Models for Context-Aware Object Detection". Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/88387.

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In this thesis, we present ContextNet, a novel general object detection framework for incorporating context cues into a detection pipeline. Current deep learning methods for object detection exploit state-of-the-art image recognition networks for classifying the given region-of-interest (ROI) to predefined classes and regressing a bounding-box around it without using any information about the corresponding scene. ContextNet is based on an intuitive idea of having cues about the general scene (e.g., kitchen and library), and changes the priors about presence/absence of some object classes. We provide a general means for integrating this notion in the decision process about the given ROI by using a pretrained network on the scene recognition datasets in parallel to a pretrained network for extracting object-level features for the corresponding ROI. Using comprehensive experiments on the PASCAL VOC 2007, we demonstrate the effectiveness of our design choices, the resulting system outperforms the baseline in most object classes, and reaches 57.5 mAP (mean Average Precision) on the PASCAL VOC 2007 test set in comparison with 55.6 mAP for the baseline.
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Bartoli, Giacomo. "Edge AI: Deep Learning techniques for Computer Vision applied to embedded systems". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16820/.

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In the last decade, Machine Learning techniques have been used in different fields, ranging from finance to healthcare and even marketing. Amongst all these techniques, the ones adopting a Deep Learning approach were revealed to outperform humans in tasks such as object detection, image classification and speech recognition. This thesis introduces the concept of Edge AI: that is the possibility to build learning models capable of making inference locally, without any dependence on expensive servers or cloud services. A first case study we consider is based on the Google AIY Vision Kit, an intelligent camera equipped with a graphic board to optimize Computer Vision algorithms. Then, we test the performances of CORe50, a dataset for continuous object recognition, on embedded systems. The techniques developed in these chapters will be finally used to solve a challenge within the Audi Autonomous Driving Cup 2018, where a mobile car equipped with a camera, sensors and a graphic board must recognize pedestrians and stop before hitting them.

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Espis, Andrea. "Object detection and semantic segmentation for assisted data labeling". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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The automation of data labeling tasks is a solution to the errors and time costs related to human labeling. In this thesis work CenterNet, DeepLabV3, and K-Means applied to the RGB color space, are deployed to build a pipeline for Assisted data labeling: a semi-automatic process to iteratively improve the quality of the annotations. The proposed pipeline pointed out a total of 1547 wrong and missing annotations when applied to a dataset originally containing 8,300 annotations. Moreover, the quality of each annotation has been drastically improved, and at the same time, more than 600 hours of work have been saved. The same models have also been used to address the real-time Tire inspection task, regarding the detection of markers on the surface of tires. According to the experiments, the combination of DeepLabV3 output and post-processing based on the area and shape of the predicted blobs, achieves a maximum of mean Precision 0.992, with mean Recall 0.982, and a maximum of mean Recall 0.998, with mean Precision 0.960.

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Norrstig, Andreas. "Visual Object Detection using Convolutional Neural Networks in a Virtual Environment". Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156609.

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Visual object detection is a popular computer vision task that has been intensively investigated using deep learning on real data. However, data from virtual environments have not received the same attention. A virtual environment enables generating data for locations that are not easily reachable for data collection, e.g. aerial environments. In this thesis, we study the problem of object detection in virtual environments, more specifically an aerial virtual environment. We use a simulator, to generate a synthetic data set of 16 different types of vehicles captured from an airplane. To study the performance of existing methods in virtual environments, we train and evaluate two state-of-the-art detectors on the generated data set. Experiments show that both detectors, You Only Look Once version 3 (YOLOv3) and Single Shot MultiBox Detector (SSD), reach similar performance quality as previously presented in the literature on real data sets. In addition, we investigate different fusion techniques between detectors which were trained on two different subsets of the dataset, in this case a subset which has cars with fixed colors and a dataset which has cars with varying colors. Experiments show that it is possible to train multiple instances of the detector on different subsets of the data set, and combine these detectors in order to boost the performance.

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Dickens, James. "Depth-Aware Deep Learning Networks for Object Detection and Image Segmentation". Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42619.

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The rise of convolutional neural networks (CNNs) in the context of computer vision has occurred in tandem with the advancement of depth sensing technology. Depth cameras are capable of yielding two-dimensional arrays storing at each pixel the distance from objects and surfaces in a scene from a given sensor, aligned with a regular color image, obtaining so-called RGBD images. Inspired by prior models in the literature, this work develops a suite of RGBD CNN models to tackle the challenging tasks of object detection, instance segmentation, and semantic segmentation. Prominent architectures for object detection and image segmentation are modified to incorporate dual backbone approaches inputting RGB and depth images, combining features from both modalities through the use of novel fusion modules. For each task, the models developed are competitive with state-of-the-art RGBD architectures. In particular, the proposed RGBD object detection approach achieves 53.5% mAP on the SUN RGBD 19-class object detection benchmark, while the proposed RGBD semantic segmentation architecture yields 69.4% accuracy with respect to the SUN RGBD 37-class semantic segmentation benchmark. An original 13-class RGBD instance segmentation benchmark is introduced for the SUN RGBD dataset, for which the proposed model achieves 38.4% mAP. Additionally, an original depth-aware panoptic segmentation model is developed, trained, and tested for new benchmarks conceived for the NYUDv2 and SUN RGBD datasets. These benchmarks offer researchers a baseline for the task of RGBD panoptic segmentation on these datasets, where the novel depth-aware model outperforms a comparable RGB counterpart.

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Solini, Arianna. "Applicazione di Deep Learning e Computer Vision ad un Caso d'uso aziendale: Progettazione, Risoluzione ed Analisi". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Nella computer vision, sono oramai più di dieci anni che si parla di Machine Learning (ML), con l'obiettivo di creare sistemi autonomi che siano in grado di realizzare modelli approssimati della realtà tridimensionale partendo da immagini bidimensionali. Grazie a questa capacità si possono interpretare e comprendere le immagini, emulando la vista umana. Molti ricercatori hanno creato reti neurali in grado di sfidarsi su grandi dataset di milioni di immagini e, come conseguenza, si è ottenuto il continuo miglioramento delle performance di classificazione di immagini da parte delle reti e la capacità di individuare il framework più adatto per ogni situazione, ottenendo risultati il più possibile performanti, veloci e accurati. Numerose aziende in tutto il mondo fanno uso di Machine Learning e computer vision, spaziando dal controllo qualità, all'assistenza diretta a persone che lavorano su attività ripetitive e spesso stancanti. Il lavoro di tesi è stato realizzato nel corso di un tirocinio presso Injenia (azienda informatica italiana partner Google) ed è stato svolto nell'ambito di un progetto industriale commissionato ad Injenia da parte di una multi-utility italiana. Il progetto prevedeva l'utilizzo di uno o più modelli di ML in ambito computer vision e, a tal fine, è stata portata avanti un'indagine su più fronti per indirizzare le scelte durante il processo di sviluppo. Una parte dei risultati dell'indagine ha fornito informazioni utili all'ottimizzazione del modello di ML utilizzato. Un'altra parte è stata utilizzata per il fine-tuning di un modello di ML (già pre-allenato), applicando quindi il principio di transfer learning al dataset di immagini fornite dalla multi-utility. Lo scopo della tesi è, quindi, quello di presentare lo sviluppo e l'applicazione di tecniche di Machine Learning, Deep Learning e computer vision ad un caso d'uso aziendale concreto.

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Cuan, Bonan. "Deep similarity metric learning for multiple object tracking". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI065.

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Le suivi d’objets multiples dans une scène est une tâche importante dans le domaine de la vision par ordinateur, et présente toujours de très nombreux verrous. Les objets doivent être détectés et distingués les uns des autres de manière continue et simultanée. Les approches «suivi par détection» sont largement utilisées, où la détection des objets est d’abord réalisée sur toutes les frames, puis le suivi est ramené à un problème d’association entre les détections d’un même objet et les trajectoires identifiées. La plupart des algorithmes de suivi associent des modèles de mouvement et des modèles d’apparence. Dans cette thèse, nous proposons un modèle de ré-identification basé sur l’apparence et utilisant l’apprentissage de métrique de similarité. Nous faisons tout d’abord appel à un réseau siamois profond pour apprendre un maping de bout en bout, des images d’entrée vers un espace de caractéristiques où les objets sont mieux discriminés. De nombreuses configurations sont évaluées, afin d’en déduire celle offrant les meilleurs scores. Le modèle ainsi obtenu atteint des résultats de ré-identification satisfaisants comparables à l’état de l’art. Ensuite, notre modèle est intégré dans un système de suivi d’objets multiples pour servir de guide d’apparence pour l’association des objets. Un modèle d’apparence est établi pour chaque objet détecté s’appuyant sur le modèle de ré-identification. Les similarités entre les objets détectés sont alors exploitées pour la classification. Par ailleurs, nous avons étudié la coopération et les interférences entre les modèles d’apparence et de mouvement dans le processus de suivi. Un couplage actif entre ces 2 modèles est proposé pour améliorer davantage les performances du suivi, et la contribution de chacun d’eux est estimée en continue. Les expérimentations menées dans le cadre du benchmark «Multiple Object Tracking Challenge» ont prouvé l’efficacité de nos propositions et donné de meilleurs résultats de suivi que l’état de l’art
Multiple object tracking, i.e. simultaneously tracking multiple objects in the scene, is an important but challenging visual task. Objects should be accurately detected and distinguished from each other to avoid erroneous trajectories. Since remarkable progress has been made in object detection field, “tracking-by-detection” approaches are widely adopted in multiple object tracking research. Objects are detected in advance and tracking reduces to an association problem: linking detections of the same object through frames into trajectories. Most tracking algorithms employ both motion and appearance models for data association. For multiple object tracking problems where exist many objects of the same category, a fine-grained discriminant appearance model is paramount and indispensable. Therefore, we propose an appearance-based re-identification model using deep similarity metric learning to deal with multiple object tracking in mono-camera videos. Two main contributions are reported in this dissertation: First, a deep Siamese network is employed to learn an end-to-end mapping from input images to a discriminant embedding space. Different metric learning configurations using various metrics, loss functions, deep network structures, etc., are investigated, in order to determine the best re-identification model for tracking. In addition, with an intuitive and simple classification design, the proposed model achieves satisfactory re-identification results, which are comparable to state-of-the-art approaches using triplet losses. Our approach is easy and fast to train and the learned embedding can be readily transferred onto the domain of tracking tasks. Second, we integrate our proposed re-identification model in multiple object tracking as appearance guidance for detection association. For each object to be tracked in a video, we establish an identity-related appearance model based on the learned embedding for re-identification. Similarities among detected object instances are exploited for identity classification. The collaboration and interference between appearance and motion models are also investigated. An online appearance-motion model coupling is proposed to further improve the tracking performance. Experiments on Multiple Object Tracking Challenge benchmark prove the effectiveness of our modifications, with a state-of-the-art tracking accuracy

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Chen, Zhe. "Augmented Context Modelling Neural Networks". Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20654.

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Contexts provide beneficial information for machine-based image understanding tasks. However, existing context modelling methods still cannot fully exploit contexts, especially for object recognition and detection. In this thesis, we develop augmented context modelling neural networks to better utilize contexts for different object recognition and detection tasks. Our contributions are two-fold: 1) we introduce neural networks to better model instance-level visual relationships; 2) we introduce neural network-based algorithms to better utilize contexts from 3D information and synthesized data. In particular, to augment the modelling of instance-level visual relationships, we propose a context refinement network and an encapsulated context modelling network for object detection. In the context refinement study, we propose to improve the modeling of visual relationships by introducing overlap scores and confidence scores of different regions. In addition, in the encapsulated context modelling study, we boost the context modelling performance by exploiting the more powerful capsule-based neural networks. To augment the modeling of contexts from different sources, we propose novel neural networks to better utilize 3D information and synthesis-based contexts. For the modelling of 3D information, we mainly investigate the modelling of LiDAR data for road detection and the depth data for instance segmentation, respectively. In road detection, we develop a progressive LiDAR adaptation algorithm to improve the fusion of 3D LiDAR data and 2D image data. Regarding instance segmentation, we model depth data as context to help tackle the low-resolution annotation-based training problem. Moreover, to improve the modelling of synthesis-based contexts, we devise a shape translation-based pedestrian generation framework to help improve the pedestrian detection performance.

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Más fuentes

Libros sobre el tema "Deep Learning, Computer Vision, Object Detection"

Escriva, David Millan, Roy Shilkrot, Prateek Joshi y Vinicius G. Mendonca. Building Computer Vision Projects with OpenCV 4 and C++: Implement complex computer vision algorithms and explore deep learning and face detection. Packt Publishing, 2019.

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Learn OpenCV 4. 5 with Python 3. 7 by Examples: Implement Computer Vision Algorithms Provided by OpenCV 4. 5 with Python 3. 7 for Image Processing, Object Detection and Machine Learning. Independently Published, 2021.

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Mehta, Vaishali, Dolly Sharma, Monika Mangla, Anita Gehlot, Rajesh Singh y Sergio Márquez Sánchez, eds. Challenges and Opportunities for Deep Learning Applications in Industry 4.0. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150360601220101.

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The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications. Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.

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Capítulos de libros sobre el tema "Deep Learning, Computer Vision, Object Detection"

Verdhan, Vaibhav. "Object Detection Using Deep Learning". En Computer Vision Using Deep Learning, 141–85. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6616-8_5.

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Li, Kaidong, Wenchi Ma, Usman Sajid, Yuanwei Wu y Guanghui Wang. "Object Detection with Convolutional Neural Networks". En Deep Learning in Computer Vision, 41–62. First edition. | Boca Raton, FL : CRC Press/Taylor and Francis, 2020. |: CRC Press, 2020. http://dx.doi.org/10.1201/9781351003827-2.

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Choudhary, Sachi, Rashmi Sharma y Gargeya Sharma. "Object Detection Frameworks and Services in Computer Vision". En Object Detection with Deep Learning Models, 23–47. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003206736-2.

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Ansari, Shamshad. "Deep Learning in Object Detection". En Building Computer Vision Applications Using Artificial Neural Networks, 219–307. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5887-3_6.

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Kim, Jongpil y Vladimir Pavlovic. "A Shape-Based Approach for Salient Object Detection Using Deep Learning". En Computer Vision – ECCV 2016, 455–70. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46493-0_28.

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Kehl, Wadim, Fausto Milletari, Federico Tombari, Slobodan Ilic y Nassir Navab. "Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation". En Computer Vision – ECCV 2016, 205–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46487-9_13.

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Wu, Falin, Guopeng Zhou, Jiaqi He, Haolun Li, Yushuang Liu y Gongliu Yang. "Efficient Object Detection and Classification of Ground Objects from Thermal Infrared Remote Sensing Image Based on Deep Learning". En Pattern Recognition and Computer Vision, 165–75. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88013-2_14.

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Liao, Shirong, Pan Zhou, Lianglin Wang y Songzhi Su. "Reading Digital Numbers of Water Meter with Deep Learning Based Object Detector". En Pattern Recognition and Computer Vision, 38–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31654-9_4.

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Grza̧bka, Marcin, Marcin Iwanowski y Grzegorz Sarwas. "On the Influence of Image Features on the Performance of Deep Learning Models in Human-Object Interaction Detection". En Computer Vision and Graphics, 165–79. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-22025-8_12.

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Kapoor, Navpreet Singh, Mansimar Anand, Priyanshu, Shailendra Tiwari, Shivendra Shivani y Raman Singh. "Real Time Face Detection-based Automobile Safety System using Computer Vision and Supervised Machine Learning". En Advancement of Deep Learning and its Applications in Object Detection and Recognition, 63–85. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003393658-4.

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Actas de conferencias sobre el tema "Deep Learning, Computer Vision, Object Detection"

Brust, Clemens-Alexander, Christoph Käding y Joachim Denzler. "Active Learning for Deep Object Detection". En 14th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007248601810190.

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Li, Guanbin y Yizhou Yu. "Deep Contrast Learning for Salient Object Detection". En 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.58.

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Dakhil, Radhwan Adnan y Ali Retha Hasoon Khayeat. "Review on Deep Learning Techniques for Underwater Object Detection". En 3rd International Conference on Data Science and Machine Learning (DSML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121505.

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Repair and maintenance of underwater structures as well as marine science rely heavily on the results of underwater object detection, which is a crucial part of the image processing workflow. Although many computer vision-based approaches have been presented, no one has yet developed a system that reliably and accurately detects and categorizes objects and animals found in the deep sea. This is largely due to obstacles that scatter and absorb light in an underwater setting. With the introduction of deep learning, scientists have been able to address a wide range of issues, including safeguarding the marine ecosystem, saving lives in an emergency, preventing underwater disasters, and detecting, spooring, and identifying underwater targets. However, the benefits and drawbacks of these deep learning systems remain unknown. Therefore, the purpose of this article is to provide an overview of the dataset that has been utilized in underwater object detection and to present a discussion of the advantages and disadvantages of the algorithms employed for this purpose.

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BAI, Yuqi y Zhaohui MENG. "Feature Maps Channel Augmentation for Object Detection". En 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2020. http://dx.doi.org/10.1109/cvidl51233.2020.00031.

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Shanahan, James G. "Introduction to Computer Vision and Realtime Deep Learning-based Object Detection". En CIKM '20: The 29th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3340531.3412177.

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Liu, Liqiang, Shian Wei, Long Jiang y Yatao Wang. "Weighted Aggregating Feature Pyramid Network for Object Detection". En 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2020. http://dx.doi.org/10.1109/cvidl51233.2020.00-73.

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Choi, Jiwoong, Ismail Elezi, Hyuk-Jae Lee, Clement Farabet y Jose M. Alvarez. "Active Learning for Deep Object Detection via Probabilistic Modeling". En 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.01010.

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Zhao, Yuanzhang y Shengling Geng. "Object detection of face mask recognition based on improved faster rcnn". En 2nd International Conference on Computer Vision, Image and Deep Learning, editado por Fengjie Cen y Badrul Hisham bin Ahmad. SPIE, 2021. http://dx.doi.org/10.1117/12.2604524.

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Sun, Yue, Shaobo Lin y Long Chen. "A Novel Two-path Backbone Network for Object Detection". En 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2020. http://dx.doi.org/10.1109/cvidl51233.2020.00-91.

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Jaffari, Rabeea, Manzoor Ahmed Hashmani, Constantino Carlos Reyes-Aldasoro, Norshakirah Aziz y Syed Sajjad Hussain Rizvi. "Deep Learning Object Detection Techniques for Thin Objects in Computer Vision: An Experimental Investigation". En 2021 7th International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2021. http://dx.doi.org/10.1109/iccar52225.2021.9463487.

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Informes sobre el tema "Deep Learning, Computer Vision, Object Detection"

Bragdon, Sophia, Vuong Truong y Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), noviembre de 2022. http://dx.doi.org/10.21079/11681/45902.

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The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a dataset that consists of thermal images containing buried objects paired with the corresponding environmental and meteorological conditions, we employ a machine learning approach to determine which environmental conditions are the most impactful on the visibility of the buried objects. We find the key environmental conditions include incoming shortwave solar radiation, soil volumetric water content, and average air temperature. For each image, ROIs are computed using a computer vision approach and these ROIs are coupled with the most important environmental conditions to form the input for the classification algorithm. The environmentally informed classification algorithm produces a decision on whether the ROI contains a buried object by simultaneously learning on the ROIs with a classification neural network and on the environmental data using a tabular neural network. On a given set of ROIs, we have shown that the environmentally informed classification approach improves the detection of buried objects within the ROIs.

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Alhasson, Haifa F. y Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, noviembre de 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.

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Review question / Objective: A significant amount of research has been conducted to detect and recognize diabetic foot ulcers (DFUs) using computer vision methods, but there are still a number of challenges. DFUs detection frameworks based on machine learning/deep learning lack systematic reviews. With Machine Learning (ML) and Deep learning (DL), you can improve care for individuals at risk for DFUs, identify and synthesize evidence about its use in interventional care and management of DFUs, and suggest future research directions. Information sources: A thorough search of electronic databases such as Science Direct, PubMed (MIDLINE), arXiv.org, MDPI, Nature, Google Scholar, Scopus and Wiley Online Library was conducted to identify and select the literature for this study (January 2010-January 01, 2023). It was based on the most popular image-based diagnosis targets in DFu such as segmentation, detection and classification. Various keywords were used during the identification process, including artificial intelligence in DFu, deep learning, machine learning, ANNs, CNNs, DFu detection, DFu segmentation, DFu classification, and computer-aided diagnosis.

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