Multi-Object Tracking with Grayscale Spatial-Temporal Features

Abstract

In recent multiple object tracking (MOT) research, there have not been many traditional methods and optimizations for matching. Most of today’s popular tracking methods are implemented using deep learning. But many monitoring devices do not have high computing power, so real-time tracking via neural networks is difficult. Furthermore, matching takes less time than detection and embedding, but it still takes some time, especially for many targets in a scene. Therefore, in order to solve these problems, we propose a new method by using grayscale maps to obtain spatial-temporal features based on traditional methods. Using this method allows us to directly find the position and region in previous frames of the target and significantly reduce the number of IDs that the target needs to match. At the same time, compared to some end-to-end paradigms, our method can quickly obtain spatial-temporal features using traditional methods, which reduces some calculations. Further, we joined embedding and matching to further reduce the time spent on tracking. Our method reduces the calculations in feature extraction and reduces unnecessary matching in the matching stage. Our method was evaluated on benchmark dataset MOT16, and it achieved great performance; the tracking accuracy metric MOTA reached 46.7%. The tracking FPS reached 17.6, and it ran only on a CPU without GPU acceleration.