Relevant bibliographies by topics / Cyclist detection

Academic literature on the topic 'Cyclist detection'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Cyclist detection"

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Wang, Kelong, and Wei Zhou. "Pedestrian and cyclist detection based on deep neural network fast R-CNN." International Journal of Advanced Robotic Systems 16, no. 2 (March 1, 2019): 172988141982965. http://dx.doi.org/10.1177/1729881419829651.

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In this article, a unified joint detection framework for pedestrian and cyclist is established to realize the joint detection of pedestrian and cyclist targets. Based on the target detection of fast regional convolution neural network, a deep neural network model suitable for pedestrian and cyclist detection is established. Experiments for poor detection results for small-sized targets and complex and changeable background environment; various network improvement schemes such as difficult case extraction, multilayer feature fusion, and multitarget candidate region input were designed to improve detection and to solve the problems of frequent false detections and missed detections in pedestrian and cyclist target detection. Results of experimental verification of the pedestrian and cyclist database established in Beijing’s urban traffic environment showed that the proposed joint detection method for pedestrians and cyclists can realize the stable tracking of joint detection and clearly distinguish different target categories. Therefore, an important basis for the behavior decision of intelligent vehicles is provided.
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Shahraki, Farideh Foroozandeh, Ali Pour Yazdanpanah, Emma E. Regentova, and Venkatesan Muthukumar. "A Trajectory Based Method of Automatic Counting of Cyclist in Traffic Video Data." International Journal on Artificial Intelligence Tools 26, no. 04 (August 2017): 1750015. http://dx.doi.org/10.1142/s0218213017500154.

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Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. One of the important factors that influence cyclists safety is their counts. In comparison to other road users, such as cars and pedestrians, the automated cyclist data collection is relatively a new research area. In this work, we develop a vision-based method for gathering cyclist count data at intersections and road segments. We implement a robust cyclist detection method based on a combination of classification features. We implement a multi-object tracking method based on the Kernelized Correlation Filters (KCF) in cooperation with the bipartite graph matching algorithm to track multiple cyclists. Then, a trajectory rebuilding method and a trajectory comparison model are applied to refine the accuracy of tracking and counting. The proposed method is the first cyclist counting method, that has the ability to count cyclists under different movement patterns. The trajectory data obtained can be further utilized for cyclist behavioral modeling and safety analysis.
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Drory, Ami, Hongdong Li, and Richard Hartley. "Estimating the projected frontal surface area of cyclists from images using a variational framework and statistical shape and appearance models." Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology 231, no. 3 (May 10, 2017): 169–83. http://dx.doi.org/10.1177/1754337117705489.

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We present a computer vision-based approach to estimating the projected frontal surface area (pFSA) of cyclists from unconstrained images. Wind tunnel studies show a reduction in cyclists’ aerodynamic drag through manipulation of the cyclist’s pose. Whilst the mechanism by which reduction is achieved remains unknown, it is widely accepted in the literature that the drag is proportional to the cyclist’s pFSA. This paper describes a repeatable automatic method for pFSA estimation for the study of its relationship with aerodynamic drag in cyclists. The proposed approach is based on finding object boundaries in images. An initialised curve dynamically evolves in the image to minimise an energy function designed to force the curve to gravitate towards image features. To overcome occlusions and pose variation, we use a statistical cyclist shape and appearance models as priors to encourage the evolving curve to arrive at the desired solution. Contour initialisation is achieved using a discriminative object detection method based on offline supervised learning that yields a cyclist classifier. Once an instance of a cyclist is detected in an image and segmented, the pFSA is calculated from the area of the final curve. Applied to two challenging datasets of cyclist images, for cyclist detection our method achieves precision scores of 1.0 and 0.96 and recall scores of 0.68 and 0.83 on the wind tunnel and cyclists-in-natura datasets, respectively. For cyclist segmentation, it achieves 0.88 and 0.92 scores for the mean dice similarity coefficient metric on the two datasets, respectively. We discuss the performance of our method under occlusion, orientation, and pose conditions. Our method successfully estimates pFSA of cyclists and opens new vistas for exploration of the relationship between pFSA and aerodynamic drag.
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Ahmed, Sarfraz, M. Nazmul Huda, Sujan Rajbhandari, Chitta Saha, Mark Elshaw, and Stratis Kanarachos. "Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey." Applied Sciences 9, no. 11 (June 6, 2019): 2335. http://dx.doi.org/10.3390/app9112335.

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As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated.
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Radová, Zuzana, and Luboš Nouzovský. "Measuring of Cyclist Impact Dynamics." Applied Mechanics and Materials 821 (January 2016): 456–63. http://dx.doi.org/10.4028/www.scientific.net/amm.821.456.

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The contribution is aimed at detection of cyclists’ dynamics in standard and non-standard situations. From forensic experts point of view there are significant both, ie. riding dynamics of cyclist and also post-crash motion in case of collision with passenger car.To determine the riding trajectory, it is necessary to devise a measuring apparatus and devise methods for measuring and processing of the collected data. This pilot study involves suggestion of available combination of several procedures such as accelerometric measuring, photogrammetry and GPS use. In addition, the pilot measurement to prove this method was performed.In the term of post-crash motion the paper deals with the biomechanical analysis of load exerted on the child cyclist in configuration typical for cyclists (sudden enter the road or the case of non-giving way; the car front vs. the left side of the cyclists). Safety contribution of the bicycle helmet.
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Eddy, Chris, Christopher de Saxe, and David Cebon. "Camera-based measurement of cyclist motion." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 7 (August 7, 2018): 1793–805. http://dx.doi.org/10.1177/0954407018789301.

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Heavy goods vehicles are overrepresented in cyclist fatality statistics in the United Kingdom relative to their proportion of total traffic volume. In particular, the statistics highlight a problem for vehicles turning left across the path of a cyclist on their inside. In this article, we present a camera-based system to detect and track cyclists in the blind spot. The system uses boosted classifiers and geometric constraints to detect cyclist wheels, and Canny edge detection to locate the ground contact point. The locations of these points are mapped into physical coordinates using a calibration system based on the ground plane. A Kalman Filter is used to track and predict the future motion of the cyclist. Full-scale tests were conducted using a construction vehicle fitted with two cameras, and the results compared with measurements from an ultrasonic-sensor system. Errors were comparable to the ultrasonic system, with average error standard deviation of 4.3 cm when the cyclist was 1.5 m from the heavy goods vehicles, and 7.1 cm at a distance of 1 m. When results were compared to manually extracted cyclist position data, errors were less than 4 cm at separations of 1.5 and 1 m. Compared to the ultrasonic system, the camera system requires simple hardware and can easily differentiate cyclists from stationary or moving background objects such as parked cars or roadside furniture. However, the cameras suffer from reduced robustness and accuracy at close range and cannot operate in low-light conditions.
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Jia, Enzo C., Jianqiang Wang, and Daiheng Ni. "An Efficient Methodology for Calibrating Traffic Flow Models Based on Bisection Analysis." Journal of Applied Mathematics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/949723.

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As urban planning becomes more sophisticated, the accurate detection and counting of pedestrians and cyclists become more important. Accurate counts can be used to determine the need for additional pedestrian walkways and intersection reorganization, among other planning initiatives. In this project, a camera-based approach is implemented to create a real-time pedestrian and cyclist counting system which is regularly accurate to 85% and often achieves higher accuracy. The approach retasks a state-of-the-art traffic camera, the Autoscope Solo Terra, for pedestrian and bicyclist counting. Object detection regions are sized to identify multiple pedestrians moving in either direction on an urban sidewalk and bicyclists in an adjacent bicycle lane. Collected results are processed in real time, eliminating the need for video storage and postprocessing. In this paper, results are presented for a pedestrian walkway for pedestrian flow up to 108 persons/min and the limitations of the implemented system are enumerated. Both pedestrian and cyclist counting accuracy of over 90% is achieved.
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Jin, Wenqiang, Srinivasan Murali, Youngtak Cho, Huadi Zhu, Tianhao Li, Rachael Thompson Panik, Anika Rimu, et al. "CycleGuard." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 4 (December 27, 2021): 1–30. http://dx.doi.org/10.1145/3494992.

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Every year 41,000 cyclists die in road traffic-related incidents worldwide [47]. One of the most startling and infuriating conflicts that cyclists experience is the so-called "right hook". It refers to a vehicle striking a cyclist heading in the same direction by turning right into the cyclist. To prevent such a crash, this work presents CycleGuard, an acoustic-based collision detection system using smartphones. It is composed of a cheap commercial off-the-shelf (COTS) portable speaker that emits imperceptible high-frequency acoustic signals and a smartphone for reflected signal reception and analysis. Since received acoustic signals bear rich information of their reflecting objects, CycleGuard applies advanced acoustic ranging techniques to extract those information for traffic analysis. Cyclists are alerted if any pending right hook crashes are detected. Real-time alerts ensure that cyclists have sufficient time to react, apply brakes, and eventually avoid the hazard. To validate the efficacy of CycleGuard, we implement a proof-of-concept prototype and carry out extensive in-field experiments under a broad spectrum of settings. Results show that CycleGuard achieves up to 95% accuracy in preventing right hook crashes and is robust to various scenarios. It is also energy-friendly to run on battery-powered smartphones.
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NAMIHIRA, Yuki, Jun MIURA, and Shuji OISHI. "Pedestrian and cyclist detection by LIDAR-camera fusion." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2016 (2016): 2P2–07b4. http://dx.doi.org/10.1299/jsmermd.2016.2p2-07b4.

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S, Anjali, and Nithin Joe. "Faster RCNN for Concurrent Pedestrian and Cyclist Detection." International Journal of Electronics and Communication Engineering 5, no. 5 (May 25, 2018): 21–24. http://dx.doi.org/10.14445/23488549/ijece-v5i5p105.

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Dissertations / Theses on the topic "Cyclist detection"

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De, Angelis Marco, Víctor Marín Puchades, Federico Fraboni, Luca Pietrantoni, and Gabriele Prati. "Negative attitudes towards cyclists influence the acceptance of an in-vehicle cyclist detection system." Elsevier, 2017. https://publish.fid-move.qucosa.de/id/qucosa%3A73236.

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The shift towards automation and safer vehicles will increasingly involve use of technological advancements such as Advanced Driver Assistance Systems (ADAS). Nevertheless, these technologies need to meet users’ perceived needs to be effectively implemented and purchased. Based on an updated version of the Technology Acceptance Model (TAM), this study analyses the main determinants of drivers’ intention to use an ADAS aimed at detecting cyclist and preventing potential collision with them through an auto-braking system. Even if the relevance of perceived usefulness, perceived ease of use and trust on the acceptance of a new system has been already discussed in literature, we considered the role of an external variable such as attitudes towards cyclists in the prediction of an ADAS aimed to improve the safety of cyclists. We administered a questionnaire measuring negative attitudes towards cyclists, trust, perceived usefulness, perceived ease of use and the behavioural intention to use the system to 480 Italian drivers. Path analysis using Bayesian estimation showed that perceived usefulness, trust in the system, and negative attitudes towards cyclists have a direct effect on the intention to use the ADAS. Considering the role of attitudes towards other road users in the intention to use new ADAS aimed to improve their safety could foster the user’s acceptance, especially for those people who express a negative representation of cyclists and are even more unlikely to accept the technology.
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Heydorn, Matthew Ryan. "Increased Cyclist Safety Using an Embedded System." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7391.

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In order to reduce bicycle-vehicle collisions, we design and implement a cost effectiveembedded system to warn cyclists of approaching vehicles. The system uses an Odroid C2 singleboard computer (SBC) to do vehicle and lane detection in real time using only vision. The system warns cyclist are warned of approaching cars using both a smartphone app and an LED indicator. Due to the limited performance of the Odroid C2 and other low power and low cost SBCs,we found that existing detection algorithms run either too slowly or do not have sufficient accuracy to be practical. Our solution to these limitations is to create a custom fully convolutional network(FCN) which is small enough to run at real time speeds on the Odroid C2 but robust enough tohave decent accuracy. We show that this FCN runs significantly faster than Tiny YOLOv3 andMobileNetv2 while getting similar accuracy when all are trained on a limited dataset. Since no dataset exists that separates the fronts of vehicles from other poses and is in the context of city and country roads, we create our own. Creating a dataset to train any detector hastraditionally been time consuming. We present and implement a way to efficiently do this usingminimal hand annotation by generating semi-synthetic images by cropping relatively few positive images into many background images. This creates a wider background class variance than wouldotherwise be possible.
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Bieshaar, Maarten [Verfasser]. "Cooperative intention detection using machine learning : advanced cyclist protection in the context of automated driving / Maarten Bieshaar." Kassel : kassel university press c/o Universität Kassel - Universitätsbibliothek, 2021. http://d-nb.info/1233244175/34.

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Abakar, Issakha Souleymane. "Algorithms for the detection and localization of pedestrians and cyclists using new generation automotive radar systems." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S159.

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En réponse au nombre toujours élevé de décès provoqués par les accidents routiers, l'industrie automobile a fait de la sécurité un sujet majeur de son activité global. Les radars automobiles qui étaient de simples capteurs pour véhicule de confort, sont devenus des éléments essentiels de la norme de sécurité routière. Le domaine de l’automobile est un domaine très exigent en terme de sécurité et les radars automobiles doivent avoir des performances de détection très élevées et doivent répondre à des nombreuses contraintes telles que la facilité de production et/ou le faible coût. Cette thèse concerne le développement d’algorithmes pour la détection et la localisation de piétons et de cyclistes pour des radars automobiles de nouvelle génération. Nous avons proposé une architecture de réseau d'antennes non uniforme optimale et des méthodes d'estimation spectrale à haute résolution permettant d’estimer avec précision la position angulaire des objets à partir de la direction d'arrivée (DoA) de leur réponse. Ces techniques sont adaptées à l'architecture du réseau d'antennes proposé et les performances sont évaluées à l'aide de données radar automobiles simulées et réelles acquises dans le cadre de scénarios spécifiques. Nous avons également proposé un détecteur de cible de collision, basé sur la décomposition en sous-espaces Doppler, dont l'objectif principal est d'identifier des cibles latérales dont les caractéristiques de trajectoire représentent potentiellement un danger de collision. Une méthode de calcul d'attribut de cible est également développée et un algorithme de classification est proposé pour discriminer les piétons, cyclistes et véhicules. Les différents algorithmes sont évalués et validés à l'aide de données radar automobiles réelles sur plusieurs scenarios
In response to the persistently high number of deaths provoked by road crashes, the automotive industry has promoted safety as a major topic in their global activity. Automotive radars have been transformed from being simple sensors for comfort vehicle, to becoming essential elements of safety standard. The design of new generations automotive radars has to face various constraints and generally proposes a compromise between reliability, robustness, manufacturability, high-performance and low cost. The main objective of this PhD thesis is to design algorithms for the detection and localization of pedestrians and cyclists using new generation automotive radars. We propose an optimal non-uniform antenna array architecture and some high resolution spectral estimation methods to accurately estimate the position of objects from the direction of arrival (DOA) of their responses to the radar. These techniques are adapted to the proposed antenna array architecture and the performance is evaluated using both simulated and real automotive radar data, acquired in the frame of specific scenarios. We propose a collision target detector, based on the orthogonality of angle-Doppler subspaces, whose main goal is to identify lateral targets, whose trajectory features represent potentially a danger of collision. A target attribute calculation method is also developed and classification algorithm is proposed to classify pedestrian, cyclists and vehicles. This classification algorithm is evaluated and validated using real automotive radar data with several scenarios
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BAKKAL, Ahmet Tansu. "Acoustic Detection of Rear Approaching Vehicles for Cyclists." Thesis, Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-25182.

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The project aims to detect rear approaching vehicles for cycslist with a low power consumption. Study focuses on acoustic features of the sound of rear approaching vehicles and examines the useful indicators to detect the vehicles. The project includes more then one correlation and reveals their success rates for as many as samples possible.
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Soames, Kieron, and Jonas Lind. "Detecting Cycles in GraphQL Schemas." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156174.

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GraphQL is a database handling API created by Facebook, that provides an effective al-ternative to REST-style architectures. GraphQL provides the ability for a client to spec-ify exactly what data it wishes to receive. A problem with GraphQL is that the freedomof creating customized requests allows data to be included several times in the response,growing the response’s size exponentially. The thesis contributes to the field of GraphQLanalysis by studying the prevalence of simple cycles in GraphQL schemas. We have im-plemented a locally-run tool and webtool using Tarjan’s and Johnson’s algorithms, thatparses the schemas, creates a directed graph and enumerates all simple cycles in the graph.A collection of schemas was analysed with the tool to collect empirical data. It was foundthat 39.73 % of the total 2094 schemas contained at least one simple cycle, with the averagenumber of cycles per schema being 4. The runtime was found to be on average 11 mil-liseconds, most of which consisted of the time for parsing the schemas. It was found that44 out of the considered schemas could not be enumerated due to containing a staggeringamount of simple cycles. It can be concluded that it is possible to test schemas for cyclicityand enumerate all simple cycles in a given schema efficiently.
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Cronje, Mercia. "Engineering process model: Detection of cycles and determination of paths." Thesis, Stellenbosch : University of Stellenbosch, 2006. http://hdl.handle.net/10019.1/2376.

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Thesis (MScEng (Civil Engineering))--University of Stellenbosch, 2006.
In order to plan the engineering work of large construction projects efficiently, a model of the engineering process is required. An engineering process can be modelled by sets of persons, tasks, datasets and tools, as well as the relationships between the elements of these sets. Tasks are more often than not dependent on other tasks in the engineering process. In large projects these dependencies are not easily recognised, and if tasks are not executed in the correct sequence, costly delays may occur. The homogeneous binary relation “has to be executed before” in the set of tasks can be used to determine the logical sequence of tasks algebraically. The relation can be described by a directed graph in the set of tasks, and the logical sequence of tasks can be determined by sorting the graph topologically, if the graph is acyclic. However, in an engineering process, this graph is not necessarily acyclic since certain tasks have to be executed in parallel, causing cycles in the graph. After generating the graph in the set of tasks, it is important to fuse all the cycles. This is achieved by finding the strongly connected components of the graph. The reduced graph, in which each strongly connected component is represented by a vertex, is a directed acyclic graph. The strongly connected components may be determined by different methods, including Kosaraju’s, Tarjan’s and Gabow’s methods. Considering the “has to be executed before” graph in the set of tasks, elementary paths through the graph, i.e. paths which do not contain any vertex more than once, are useful to investigate the influence of tasks on other tasks. For example, the longest elementary path of the graph is the logical critical path. The solution of such path problems in a network may be reduced to the solution of systems of equations using path algebras. The solution of the system of equations may be determined directly, i.e. through Gauss elimination, or iteratively, through Jacobi’s or Gauss-Seidel’s methods or the forward and back substitution method. The vertex sequence of an acyclic graph can be assigned in such a way that the coefficient matrix of the system of equations is reduced to staggered form, after which the solution is found by a simple back substitution. Since an engineering process has a start and an end, it is more acyclic than cyclic. Consequently we can usually reduce a substantial part of the coefficient matrix to staggered form. Using this technique, modifications of the solution methods mentioned above were implemented, and the efficiency of the technique is determined and compared between the various methods.
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O'Connor, Matthew. "Ruminant prion disease detection and characterisation using protein misfolding cyclic amplification." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/41599/.

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Prion diseases or transmissible spongiform encephalopathies (TSE) are characterised by the accumulation of a misfolded conformer (PrPSc) of a host encoded protein (PrPC). The misfolding event that leads to the formation PrPSc can be replicated in the in vitro amplification technique, protein misfolding cyclic amplification (PMCA). This thesis focuses on the application PMCA to study multiple aspects of prion misfolding in relation to ruminant prion diseases, specifically developing techniques to detect and characterise PrPSc in scrapie and BSE infections. Utilising recombinant hamster PrP (rPrP) as substrate in PMCA, multiple genotypes of scrapie were successfully amplified in an attempt to describe a quantifiable technique applicable to a wide range of scrapie isolates. Observations of non-specific protease resistant rPrP formation was investigated with modifications to the PMCA methodology, which ultimately proved unsuccessful in reducing non-specific protease resistant rPrP. Using brain PrPC as substrate, the quantitative PMCA technique was piloted with BSE to correlate in vitro replication efficiency with infectious titre in mouse bioassay, but no correlation was identified. Atypical forms of BSE occur primarily in older cattle, are asymptomatic and thought to be spontaneous diseases. None the less, infection models in rodents and primates have identified the zoonotic potential of H-type and L-type BSE. Therefore PMCA methods were developed which were able to successfully amplify both atypical forms of BSE. In particular, sensitive detection and discrimination from classical BSE was demonstrated for H-type BSE, which has not previously been amplified in PMCA. H-type BSE could be detected in 1x10¬-12 g brain material and was discriminated from classical BSE by increased protease sensitivity, relatively high molecular weight and antibody reactivity. Evidence exists for co-infection of TSE strains, yet scrapie and BSE co-infection in an ovine host remains unaddressed. To study the disease progression and tissue dissemination of co-infections a PMCA assay capable of specifically amplifying BSE PrPSc in the presence of excess scrapie was applied to artificially mixed brain homogenates containing BSE and scrapie, and compared to current statutory strain typing methods. The PMCA was found to have sensitivity and specificity of 100% in mixes containing 0.1% BSE and 99.9% scrapie brain material, which was more effective than conventional strain typing methods. The assay was then applied to the brain, spleen and lymph of scrapie and BSE experimental co-infections in two genotypes of sheep, and to animals which belonged to a flock with endemic natural scrapie and that also received experimental BSE infections. The PMCA data demonstrated that sheep with PRNP genotype ARQ/ARQ (at amino acid positions 134, 154 and 171) were resistant to BSE in a co-infection scenario. In sheep with PRNP genotype of VRQ/ARQ, mixed infections could occur, and animals with scrapie PrPSc only in the brain could harbour BSE PrPSc in peripheral tissues. Co-infection was also possible in sheep with natural scrapie infections. The assay was compared to conventional testing methods of western blotting, PrPd profiling and immunohistochemistry and displayed superior sensitivity in BSE detection. PMCA amplification of bovine BSE isolates in ovine substrates identified several instances in which the molecular characteristics of the PrPSc was scrapie-like in terms of molecular weight, antibody reactivity and glycoform profile, and in some cases PrPSc characteristic of BSE could no longer be recovered. This occurred in a genotype specific manner, ‘molecular switching’ was only apparent in ovine substrate VRQ/VRQ in accordance with previous findings. These results raise the possibility of such an event occurring in in vivo ovine BSE infections and the zoonotic potential of these scrapie like conformers are yet to be fully addressed.
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Kolluri, Murali Mohan. "Non-parametric nonlinearity detection under broadband excitation." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573224392534571.

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Kalil, Haitham Fawzy Mohamed. "NANOMATERIALS-BASED SENSORS FOR PEROXYNITRITE DETECTION AND QUANTIFICATION." Cleveland State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=csu151336709631904.

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Books on the topic "Cyclist detection"

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Conan, Doyle Arthur. The Adventure of the Solitary Cyclist. Mankato, MN, USA: Creative Education, 1991.

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Bob, Robbins. Signal investing: Detecting powerful trends in risk and market cycles. New York: McGraw-Hill, 2012.

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Pulido, Eugenio Fuentes. Contrarreloj. Barcelona, Spain: Tusquets, 2009.

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Goodwin, Vincent. Sir Arthur Conan Doyle's The adventure of the solitary cyclist. Minneapolis, Minn: Magic Wagon, 2012.

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Moody, Greg. Derailleur: A cycling murder mystery. Boulder, Colo: VeloPress, 1999.

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Dead air: A cycling murder mystery : a novel. Boulder, CO: VeloPress, 2002.

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Moody, Greg. Deadroll: A cycling murder mystery. Boulder, Colo: VeloPress, 2001.

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Johannes, Mark Robert Stephen. Detecting and understanding marine-terrestrial linkages in a developing watershed: Nutrient cycling in the Kenai River watershed. Anchorage, Alaska: EVOS Trustee Council, 2003.

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Conan, Doyle Arthur. Quatre aventures de Sherlock Holmes: La cycliste solitaire suivi de ; Charles Auguste Milverton ; Le Gloria Scott ; Le trois-quart aile manquant. Paris: Librio, 1994.

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Hill, Laban Carrick. Spiked snow. New York: Hyperion Books for Children, 1998.

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Book chapters on the topic "Cyclist detection"

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Saranya, Karattupalayam Chidambaram, Arunkumar Thangavelu, Ashwin Chidambaram, Sharan Arumugam, and Sushant Govindraj. "Cyclist Detection Using Tiny YOLO v2." In Advances in Intelligent Systems and Computing, 969–79. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0184-5_82.

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Ahmed, Sarfraz, M. Nazmul Huda, Sujan Rajbhandari, Chitta Saha, Mark Elshaw, and Stratis Kanarachos. "Visual and Thermal Data for Pedestrian and Cyclist Detection." In Towards Autonomous Robotic Systems, 223–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25332-5_20.

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Lee, Vincent T. "Detection of Cyclic Dinucleotide Binding Proteins." In Microbial Cyclic Di-Nucleotide Signaling, 107–24. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33308-9_7.

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Waters, Christopher M. "Methods for Cyclic Di-GMP Detection." In The Second Messenger Cyclic Di-GMP, 68–75. Washington, DC, USA: ASM Press, 2014. http://dx.doi.org/10.1128/9781555816667.ch6.

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Du, Xiao-Xia, and Xiao-Dong Su. "Detection of Cyclic Dinucleotides by STING." In c-di-GMP Signaling, 59–69. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7240-1_6.

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Xu, Wentao. "Detecting Targets Without Thermal Cycling in Food: Isothermal Amplification and Hybridization." In Functional Nucleic Acids Detection in Food Safety, 185–218. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1618-9_10.

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Jenner, Mareike. "Diversifying Detection: Proliferation of Channels and Television Cycles 1980–2000." In American TV Detective Dramas, 100–123. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/9781137425669_7.

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Eldesokey, Abdelrahman, Michael Felsberg, and Fahad Shahbaz Khan. "Ellipse Detection for Visual Cyclists Analysis “In the Wild”." In Computer Analysis of Images and Patterns, 319–31. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64689-3_26.

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Jiang, Di, Hui Liu, Qiang Guo, and Caiming Zhang. "Cyclic DenseNet for Tumor Detection and Identification." In Cyberspace Safety and Security, 487–93. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37352-8_42.

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Frisén, Marianne. "Detection of Turning Points in Business Cycles." In International Encyclopedia of Statistical Science, 382–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_26.

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Conference papers on the topic "Cyclist detection"

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Tian, Wei, and Martin Lauer. "Fast Cyclist Detection by Cascaded Detector and Geometric Constraint." In 2015 IEEE 18th International Conference on Intelligent Transportation Systems - (ITSC 2015). IEEE, 2015. http://dx.doi.org/10.1109/itsc.2015.211.

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Bieshaar, Maarten, Stefan Zernetsch, Katharina Riepe, Konrad Doll, and Bernhard Sick. "Cyclist Motion State Forecasting - Going beyond Detection." In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2021. http://dx.doi.org/10.1109/ssci50451.2021.9660151.

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Masalov, Alexander, Pavel Matrenin, Jeffrey Ota, Florian Wirth, Christoph Stiller, Heath Corbet, and Eric Lee. "Specialized Cyclist Detection Dataset: Challenging Real-World Computer Vision Dataset for Cyclist Detection Using a Monocular RGB Camera." In 2019 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2019. http://dx.doi.org/10.1109/ivs.2019.8813814.

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Xiaofei Li, Fabian Flohr, Yue Yang, Hui Xiong, Markus Braun, Shuyue Pan, Keqiang Li, and Dariu M. Gavrila. "A new benchmark for vision-based cyclist detection." In 2016 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2016. http://dx.doi.org/10.1109/ivs.2016.7535515.

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Tong Li, Xianbin Cao, and Yanwu Xu. "An effective crossing cyclist detection on a moving vehicle." In 2010 8th World Congress on Intelligent Control and Automation (WCICA 2010). IEEE, 2010. http://dx.doi.org/10.1109/wcica.2010.5554979.

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Annapareddy, Navya, Emir Sahin, Sander Abraham, Md Mofijul Islam, Max DePiro, and Tariq Iqbal. "A Robust Pedestrian and Cyclist Detection Method Using Thermal Images." In 2021 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 2021. http://dx.doi.org/10.1109/sieds52267.2021.9483730.

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Du, Xiao-Ping, Hui Xiong, and Yang Li. "Have a Deep Look at Deformable Part Models for Cyclist Detection." In International Conference on Computer Science and Artificial Intelligence (CSAI2016). WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813220294_0019.

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Zernetsch, Stefan, Hannes Reichert, Viktor Kress, Konrad Doll, and Bernhard Sick. "A Holistic View on Probabilistic Trajectory Forecasting – Case Study. Cyclist Intention Detection." In 2022 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2022. http://dx.doi.org/10.1109/iv51971.2022.9827220.

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Tyler-Rodrigue, Marco, and Richard Green. "Track Cyclist Detection and Identification using Mask R-CNN and K-means Clustering." In 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2019. http://dx.doi.org/10.1109/ivcnz48456.2019.8961035.

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Saleh, Khaled, Mohammed Hossny, Ahmed Hossny, and Saeid Nahavandi. "Cyclist detection in LIDAR scans using faster R-CNN and synthetic depth images." In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017. http://dx.doi.org/10.1109/itsc.2017.8317599.

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Reports on the topic "Cyclist detection"

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Kulhandjian, Hovannes. AI-based Pedestrian Detection and Avoidance at Night using an IR Camera, Radar, and a Video Camera. Mineta Transportation Institute, November 2022. http://dx.doi.org/10.31979/mti.2022.2127.

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In 2019, the United States experienced more than 6,500 pedestrian fatalities involving motor vehicles which resulted in a 67% rise in nighttime pedestrian fatalities and only a 10% rise in daytime pedestrian fatalities. In an effort to reduce fatalities, this research developed a pedestrian detection and alert system through the application of a visual camera, infrared camera, and radar sensors combined with machine learning. The research team designed the system concept to achieve a high level of accuracy in pedestrian detection and avoidance during both the day and at night to avoid potentially fatal accidents involving pedestrians crossing a street. The working prototype of pedestrian detection and collision avoidance can be installed in present-day vehicles, with the visible camera used to detect pedestrians during the day and the infrared camera to detect pedestrians primarily during the night as well as at high glare from the sun during the day. The radar sensor is also used to detect the presence of a pedestrian and calculate their range and direction of motion relative to the vehicle. Through data fusion and deep learning, the ability to quickly analyze and classify a pedestrian’s presence at all times in a real-time monitoring system is achieved. The system can also be extended to cyclist and animal detection and avoidance, and could be deployed in an autonomous vehicle to assist in automatic braking systems (ABS).
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Camarero, J. Developing New Tools for the in vivo Generation/Screening of Cyclic Peptide Libraries. A New Combinatorial Approach for the Detection of Bacterial Toxin Inhibitors. Office of Scientific and Technical Information (OSTI), November 2006. http://dx.doi.org/10.2172/902307.

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Tesfaigzi, J., M. B. Wood, and N. F. Johnson. Expression of cyclin D{sub 1} during endotoxin-induced aleveolar type II cell hyperplasia in rat lung and the detection of apoptotic cells during the remodeling process. Office of Scientific and Technical Information (OSTI), December 1995. http://dx.doi.org/10.2172/381386.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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