Research and implementation of multi-object tracking based on vision DSP

Abstract

This paper applies a pragmatic approach to study the real-time performance effect of software design methods for multiple object tracking (MOT) based on vision digital signal processing (vision DSP). The MOT system in the paper combines target detection, the Hungarian algorithm and the Kernel correlation filter (KCF) tracker. In addition, the MOT system needs to support multiway video streams, so higher speed and storage requirements are necessary for target tracking. Therefore, we carried out some studies on how to improve tracker speed performance and reduce system resource consumption under limited system resources. In the paper, we achieved the goal in two respects. Regarding the data processing, we studied how to efficiently process tracking data by utilizing the parallel characteristics of iDMA (integrated direct memory access) and a DSP core; and regarding the data storage, we proposed a time-sharing strategy to solve the DSP local memory (data RAM) usage issue for multiple tracking objects. In addition, regarding the software design, we propose a new strategy, which includes two levels of parallel computations: the frame-level parallel computations and the tracking object-level parallel computations. The experimental results show that the KCF tracking algorithm based on vision DSP achieves not only the desired real-time tracking speed but also the expected goal of system resource utilization. Our research methods also provide a reference for algorithm embedded applications in the field of computer vision.