Academic literature on the topic 'Multimodal object detection'
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Journal articles on the topic "Multimodal object detection"
Yang, Dongfang, Xing Liu, Hao He, and Yongfei Li. "Air-to-ground multimodal object detection algorithm based on feature association learning." International Journal of Advanced Robotic Systems 16, no. 3 (May 1, 2019): 172988141984299. http://dx.doi.org/10.1177/1729881419842995.
Full textKim, Jinsoo, and Jeongho Cho. "Exploring a Multimodal Mixture-Of-YOLOs Framework for Advanced Real-Time Object Detection." Applied Sciences 10, no. 2 (January 15, 2020): 612. http://dx.doi.org/10.3390/app10020612.
Full textXiao, Shouguan, and Weiping Fu. "Visual Relationship Detection with Multimodal Fusion and Reasoning." Sensors 22, no. 20 (October 18, 2022): 7918. http://dx.doi.org/10.3390/s22207918.
Full textHong, Bowei, Yuandong Zhou, Huacheng Qin, Zhiqiang Wei, Hao Liu, and Yongquan Yang. "Few-Shot Object Detection Using Multimodal Sensor Systems of Unmanned Surface Vehicles." Sensors 22, no. 4 (February 15, 2022): 1511. http://dx.doi.org/10.3390/s22041511.
Full textLin, Che-Tsung, Yen-Yi Wu, Po-Hao Hsu, and Shang-Hong Lai. "Multimodal Structure-Consistent Image-to-Image Translation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11490–98. http://dx.doi.org/10.1609/aaai.v34i07.6814.
Full textZhang, Liwei, Jiahong Lai, Zenghui Zhang, Zhen Deng, Bingwei He, and Yucheng He. "Multimodal Multiobject Tracking by Fusing Deep Appearance Features and Motion Information." Complexity 2020 (September 25, 2020): 1–10. http://dx.doi.org/10.1155/2020/8810340.
Full textGao, Yueqing, Huachun Zhou, Lulu Chen, Yuting Shen, Ce Guo, and Xinyu Zhang. "Cross-Modal Object Detection Based on a Knowledge Update." Sensors 22, no. 4 (February 10, 2022): 1338. http://dx.doi.org/10.3390/s22041338.
Full textKniaz, V. V., and P. Moshkantseva. "OBJECT RE-IDENTIFICATION USING MULTIMODAL AERIAL IMAGERY AND CONDITIONAL ADVERSARIAL NETWORKS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-2/W1-2021 (April 15, 2021): 131–36. http://dx.doi.org/10.5194/isprs-archives-xliv-2-w1-2021-131-2021.
Full textMuresan, Mircea Paul, Ion Giosan, and Sergiu Nedevschi. "Stabilization and Validation of 3D Object Position Using Multimodal Sensor Fusion and Semantic Segmentation." Sensors 20, no. 4 (February 18, 2020): 1110. http://dx.doi.org/10.3390/s20041110.
Full textEssen, Helmut, Wolfgang Koch, Sebastian Hantscher, Rüdiger Zimmermann, Paul Warok, Martin Schröder, Marek Schikora, and Goert Luedtke. "A multimodal sensor system for runway debris detection." International Journal of Microwave and Wireless Technologies 4, no. 2 (April 2012): 155–62. http://dx.doi.org/10.1017/s1759078712000116.
Full textDissertations / Theses on the topic "Multimodal object detection"
Ramezani, Pooya. "Robustness of multimodal 3D object detection using deep learning approach for autonomous vehicles." Master's thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/68766.
Full textIn this thesis, we study the robustness of a multimodal 3D object detection model in the context of autonomous vehicles. Self-driving cars need to accurately detect and localize pedestrians and other vehicles in their 3D surrounding environment to drive on the roads safely. Robustness is one of the most critical aspects of an algorithm in the self-driving car 3D perception problem. Therefore, in this work, we proposed a method to evaluate a 3D object detector’s robustness. To this end, we have trained a representative multimodal 3D object detector on three different datasets. Afterward, we evaluated the trained model on datasets that we have proposed and made to assess the robustness of the trained models in diverse weather and lighting conditions. Our method uses two different approaches for building the proposed datasets for evaluating the robustness. In one approach, we used artificially corrupted images, and in the other one, we used the real images captured in diverse weather and lighting conditions. To detect objects such as cars and pedestrians in the traffic scenes, the multimodal model relies on images and 3D point clouds. Multimodal approaches for 3D object detection exploit different sensors such as camera and range detectors for detecting the objects of interest in the surrounding environment. We leveraged three well-known datasets in the domain of autonomous driving consist of KITTI, nuScenes, and Waymo. We conducted extensive experiments to investigate the proposed method for evaluating the model’s robustness and provided quantitative and qualitative results. We observed that our proposed method can measure the robustness of the model effectively.
Khalidov, Vasil. "Modèles de mélanges conjugués pour la modélisation de la perception visuelle et auditive." Grenoble, 2010. http://www.theses.fr/2010GRENM064.
Full textIn this thesis, the modelling of audio-visual perception with a head-like device is considered. The related problems, namely audio-visual calibration, audio-visual object detection, localization and tracking are addressed. A spatio-temporal approach to the head-like device calibration is proposed based on probabilistic multimodal trajectory matching. The formalism of conjugate mixture models is introduced along with a family of efficient optimization algorithms to perform multimodal clustering. One instance of this algorithm family, namely the conjugate expectation maximization (ConjEM) algorithm is further improved to gain attractive theoretical properties. The multimodal object detection and object number estimation methods are developed, their theoretical properties are discussed. Finally, the proposed multimodal clustering method is combined with the object detection and object number estimation strategies and known tracking techniques to perform multimodal multiobject tracking. The performance is demonstrated on simulated data and the database of realistic audio-visual scenarios (CAVA database)
ur, Réhman Shafiq. "Expressing emotions through vibration for perception and control." Doctoral thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-32990.
Full textTaktil Video
"Multi-Directional Slip Detection Between Artificial Fingers and a Grasped Object." Master's thesis, 2012. http://hdl.handle.net/2286/R.I.14851.
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M.S. Bioengineering 2012
Books on the topic "Multimodal object detection"
Ufimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.
Full textBook chapters on the topic "Multimodal object detection"
Haker, Martin, Thomas Martinetz, and Erhardt Barth. "Multimodal Sparse Features for Object Detection." In Artificial Neural Networks – ICANN 2009, 923–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_93.
Full textBrekke, Åsmund, Fredrik Vatsendvik, and Frank Lindseth. "Multimodal 3D Object Detection from Simulated Pretraining." In Communications in Computer and Information Science, 102–13. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35664-4_10.
Full textLiu, Chang, Xiaoyan Qian, Binxiao Huang, Xiaojuan Qi, Edmund Lam, Siew-Chong Tan, and Ngai Wong. "Multimodal Transformer for Automatic 3D Annotation and Object Detection." In Lecture Notes in Computer Science, 657–73. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19839-7_38.
Full textAraújo, Teresa, Guilherme Aresta, Adrian Galdran, Pedro Costa, Ana Maria Mendonça, and Aurélio Campilho. "UOLO - Automatic Object Detection and Segmentation in Biomedical Images." In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 165–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00889-5_19.
Full textZhang, Jian-Hua, Jian-Wei Zhang, Sheng-Yong Chen, and Ying Hu. "Multimodal Mixed Conditional Random Field Model for Category-Independent Object Detection." In Advances in Intelligent Systems and Computing, 629–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37835-5_54.
Full textZhou, Feng, Yong Hu, and Xukun Shen. "MFDCNN: A Multimodal Fusion DCNN Framework for Object Detection and Segmentation." In Advances in Multimedia Information Processing – PCM 2018, 3–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00764-5_1.
Full textSchneider, Lukas, Manuel Jasch, Björn Fröhlich, Thomas Weber, Uwe Franke, Marc Pollefeys, and Matthias Rätsch. "Multimodal Neural Networks: RGB-D for Semantic Segmentation and Object Detection." In Image Analysis, 98–109. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59126-1_9.
Full textRoza, Felippe Schmoeller, Maximilian Henne, Karsten Roscher, and Stephan Günnemann. "Assessing Box Merging Strategies and Uncertainty Estimation Methods in Multimodel Object Detection." In Computer Vision – ECCV 2020 Workshops, 3–10. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65414-6_1.
Full textJafri, Rabia, and Syed Abid Ali. "A Multimodal Tablet–Based Application for the Visually Impaired for Detecting and Recognizing Objects in a Home Environment." In Lecture Notes in Computer Science, 356–59. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08596-8_55.
Full textReinders, Christoph, Hanno Ackermann, Michael Ying Yang, and Bodo Rosenhahn. "Learning Convolutional Neural Networks for Object Detection with Very Little Training Data." In Multimodal Scene Understanding, 65–100. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-817358-9.00010-x.
Full textConference papers on the topic "Multimodal object detection"
Mukherjee, Dibyendu, Ashirbani Saha, Q. M. Jonathan Wu, and Wei Jiang. "Multimodal 3D histogram for moving object detection." In 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2014. http://dx.doi.org/10.1109/smc.2014.6974285.
Full textSindagi, Vishwanath A., Yin Zhou, and Oncel Tuzel. "MVX-Net: Multimodal VoxelNet for 3D Object Detection." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8794195.
Full textYuan, Chunyu, and Sos S. Agaian. "BiThermalNet: a lightweight network with BNN RPN for thermal object detection." In Multimodal Image Exploitation and Learning 2022, edited by Sos S. Agaian, Sabah A. Jassim, Stephen P. DelMarco, and Vijayan K. Asari. SPIE, 2022. http://dx.doi.org/10.1117/12.2618104.
Full textGong, Dihong, and Daisy Zhe Wang. "Extracting Visual Knowledge from the Web with Multimodal Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/238.
Full textMannella, Andrea, Vittorio Sala, and Davide Maria Fabris. "Metrological investigation of the localization uncertainty of object detection methodologies." In Multimodal Sensing and Artificial Intelligence: Technologies and Applications II, edited by Shahriar Negahdaripour, Ettore Stella, Dariusz Ceglarek, and Christian Möller. SPIE, 2021. http://dx.doi.org/10.1117/12.2593286.
Full textJia, Ziyu, Youfang Lin, Jing Wang, Xuehui Wang, Peiyi Xie, and Yingbin Zhang. "SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/360.
Full textAstapov, Sergei, Jurgo-Soren Preden, Johannes Ehala, and Andri Riid. "Object detection for military surveillance using distributed multimodal smart sensors." In 2014 International Conference on Digital Signal Processing (DSP). IEEE, 2014. http://dx.doi.org/10.1109/icdsp.2014.6900688.
Full textDrost, Bertram, and Slobodan Ilic. "3D Object Detection and Localization Using Multimodal Point Pair Features." In 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT). IEEE, 2012. http://dx.doi.org/10.1109/3dimpvt.2012.53.
Full textAbbott, Rachael, Neil Robertson, Jesus Martinez-del-Rincon, and Barry Connor. "Multimodal object detection using unsupervised transfer learning and adaptation techniques." In Artificial Intelligence and Machine Learning in Defense Applications, edited by Judith Dijk. SPIE, 2019. http://dx.doi.org/10.1117/12.2532794.
Full textXu, Shaoqing, Dingfu Zhou, Jin Fang, Junbo Yin, Zhou Bin, and Liangjun Zhang. "FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection." In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, 2021. http://dx.doi.org/10.1109/itsc48978.2021.9564951.
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