Use of improved Deep Learning and DeepSORT for Vehicle estimation

Abstract: Intelligent Traffic System (ITS) has high application value in nowadays vehicle surveillance and future applications such as automated driving. The crucial part of ITS is to detect and track vehicles in real-time video stream with high accuracy and low GPU consumption. In this project, we select the YOLO version4 (YOLOv4) one-stage deep learning detector to generate bounding boxes with vehicle classes and location as well as confidence value, we select Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) tracker to track vehicles using the output of YOLOv4 detector. Furthermore, in order to make the detector more adaptive to practical use, especially when the vehicle is small or obscured, we improved the detector’s structure by adding attention mechanisms and reducing parameters to detect vehicles with relatively high accuracy and low GPU memory usage. With the baseline model, results show that the YOLOv4 and DeepSORT vehicle detection could achieve 82.4% mean average precision among three vehicle classes with 63.945 MB parameters under 19.98 frames per second. After optimization, the improved model could achieve 85.84% mean average precision among three detection classes with 44.158MB parameters under 18.65 frames per second. Compared with original YOLOv4, the improved YOLOv4 detector could increase the mean average precision by 3.44% and largely reduced the parameters by 30.94% as well as maintaining high detection speed. This proves the validity and high applicability of the proposed improved YOLOv4 detector.