| |
| This paper proposes a practical multiple object tracking system based on DeepSort (Simple Online and Realtime Tracking with a Deep Association Metric). This system uses the tracking-by-detection method as the framework, which divides the optimization processes into two parts: detection and tracking. As for the overall performance of the algorithm, detection quality is a key element for it. According to the comparison in terms of speed and performance, suitable detectors are SDP-CRC and Yolov3 in multi-object tracking scenes. Additionally, this project has proposed a new pre-processing method based on NMS (non maximum suppression). This method has improved tracking performance by up to 3.5\% in terms of MOTA. As for tracking components, this project replaces a new estimation method based on visual object trackers (SiamRPN) with traditional Kalman motion estimation method. The experimental results show that this method has improved MOTA (Multiple Object Tracking Accuracy) by 0.3\%. Besides, the speed performance has decreased nearly 500\%. That’s because this experiment should be carried out more finely. In the next step, the performance of this method can be improved by updating the template frame appropriately, using appearance information provided by SiamRPN tracker for matching process and improving the robustness of the tracker. Additionally, this project has optimized detection-to-tracker association algorithm by using their positional and velocity relationship. It has reduced the complexity of the algorithm by avoiding multi-dimensional matrix operations. |
|
| Keywords:Artificial Intelligence; Computer Vision; Multiple Object Tracking; Data Association |
|
