Distributed and collaborative real-time vehicle detection and classification over the video streams

Kul, Seda; Eken, Süleyman; Sayar, Ahmet

doi:10.1177/1729881417720782

Cited by 33 publications

(15 citation statements)

References 22 publications

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“…Interframe differencing [8] is based on the difference of two or more successive image frames, whereas background subtraction is based on the difference of moving vehicles and stationary background [2]. As for the classification, it could be realize by giving the geometric features or binary features to different classifiers, such as support vector machine (SVM), artificial neural network (ANN), and AdaBoost [9].…”

Section: A Based On Motion Featuresmentioning

confidence: 99%

A Video-Based Method With Strong-Robustness for Vehicle Detection and Classification Based on Static Appearance Features and Motion Features

Chen

2021

IEEE Access

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Vehicle detection and classification plays an important role in intelligent transportation system. Compared with traditional detectors, the detection and classification based on traffic surveillance video shows a huge advantage in its flexibility and continuity. However, to get wide applicability and strong robustness, most current methods focus on improving the accuracy of detectors by adjusting network parameters constantly, or increasing the size of training sets, which challenges the collection and labeling of data, the performance of computers, the scope of application and so on. Moreover, the unique continuity characteristic of the video, which can be used to describe the motion features of vehicle, is often ignored. Take these facts into account, this paper proposed a video-based vehicle detection and classification method, which is based on static appearance features and motion features both. Four detectors of different performance were trained with small training sets, and the designed algorithms for the remove, selection and reorganization of detected objects contribute to obtaining the optimal results of detection and classification. The experiment results show that the proposed method is able to detect and classify vehicles with more than 0.95 accuracy dealing with different road environments.

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Section: A Based On Motion Featuresmentioning

confidence: 99%

A Video-Based Method With Strong-Robustness for Vehicle Detection and Classification Based on Static Appearance Features and Motion Features

Chen

2021

IEEE Access

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“…Finally, SVM was trained by extracted features to verify vehicle category. Kul [25] et al designed a system for traffic control, they first utilized background subtraction method to extract the vehicle foregrounds. Then, they extracted geometry-based feature and utilize PCA to reduce feature dimension.…”

Section: The Related Workmentioning

confidence: 99%

A Robust Vehicle Detection Scheme for Intelligent Traffic Surveillance Systems in Smart Cities

et al. 2020

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Accurately obtaining road vehicle information is important in intelligent traffic surveillance systems for smart cities. Especially smart vehicle detection is recognized as the critical research issue of intelligent traffic surveillance systems. In this paper, a robust real-time vehicle detection method for the system is proposed. The method combines background subtraction model MOG2(Mixture of Gaussians) with a modified SqueezeNet model (H-SqueezeNet). The MOG2 model is utilized to create scale-insensitive Region of Interest (RoIs) from video frames. H-SqueezeNet is then proposed to accurately identify vehicle category. The effectiveness of the method was verified in CDnet2014 dataset, UA-DETRAC dataset and video data from a traffic intersection in Suzhou, China. The experiment results show that the method can achieves excellent detection accuracy in traffic surveillance systems, and achieve an average detection speed of 39.1 FPS.

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“…An ROS consists of a number of independent nodes that communicate with each other using a publish/subscribe messaging pattern. 41 For example, a node might implement a particular sensor’s driver, and publishes its acquired data in a stream of messages. These messages are then available for consumption by all other nodes that subscribe to that stream, including filters, loggers, and also higher level systems such as pathfinding, collision detection, etc.…”

Section: Educational Approachmentioning

confidence: 99%

A reproducible educational plan to teach mini autonomous race car programming

Eken

Şara

Satılmış

et al. 2020

International Journal of Electrical Engineering & Education

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As autonomous cars and complex features of them grow in popularity, ensuring that analyses and capabilities are reproducible and repeatable has taken on importance in education plans too. This paper describes a reproducible research plan on mini autonomous race car programming. This educational plan is designed and implemented as part of a summer internship program at Kocaeli University and it consists of theoretical courses and laboratory assignments. A literate programming approach with the Python language is used for programming the race car. To assess the educational program’s impact on the learning process and to evaluate the acceptance and satisfaction level of students, they answered an electronic questionnaire after finishing the program. According to students’ feedback, the reproducible educational program is useful for learning and consolidating new concepts of mini autonomous car programming.

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Distributed and collaborative real-time vehicle detection and classification over the video streams

Cited by 33 publications

References 22 publications

A Video-Based Method With Strong-Robustness for Vehicle Detection and Classification Based on Static Appearance Features and Motion Features

A Video-Based Method With Strong-Robustness for Vehicle Detection and Classification Based on Static Appearance Features and Motion Features

A Robust Vehicle Detection Scheme for Intelligent Traffic Surveillance Systems in Smart Cities

A reproducible educational plan to teach mini autonomous race car programming

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