Improved Vehicle Detection and Tracking Using YOLO and CSRT

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Improved Vehicle Detection and Tracking Using YOLO and CSRT

Amitha, I. C. / Narayanan, N. K.

Vehicle detection and tracking plays an important role in Intelligent Transportation Systems (ITS). This paper reports an improved vehicle detection and tracking performance of combined You Only Look Once (YOLO) and Discriminative Correlation Filter (with Channel and Spatial Reliability) (CSRT). CSRT is mainly used for face prediction and moving object detection. The proposed system uses CSRT for vehicle tracking, particularly for cars, buses, and trucks. To perform the vehicle detection task, we have used the YOLO v3 pre-trained model. The accuracy and effectiveness of our vehicle detection and tracking system are tested with 8 different commonly available trackers in various publicly available traffic videos. Experimental results show that the CSRT gives the best performance among all the other trackers. CSRT gives 100% accuracy in all the four publicly available traffic videos even though vehicles with poor lighting conditions.

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Document information

Title :

Improved Vehicle Detection and Tracking Using YOLO and CSRT

Additional title:

Lect. Notes in Networks, Syst.

Contributors:

Sharma, Harish (editor) / Gupta, Mukesh Kumar (editor) / Tomar, G. S. (editor) / Lipo, Wang (editor) / Amitha, I. C. (author) / Narayanan, N. K. (author)

Published in:

Communication and Intelligent Systems ; Chapter : 35 ; 435-446

Lecture Notes in Networks and Systems ; 204

Publication date :

2021-06-29

Size :

12 pages

ISBN :

978-981-16-1089-9 , 978-981-16-1088-2

ISSN :

2367-3389 , 2367-3370

DOI :