Deep-pipelined FPGA Implementation of Real-time Object Tracking using a Particle Filter

Abstract

This paper presents a real-time FPGA implementation of the posterior system state estimation in dynamic models, which is developed using particle filter algorithm. Specially, our system is constructed by parallel resampling (FO-resampling) algorithm on a stream-based architecture. To be precise, the resampling is accomplished in a valid pixel area of an input image frame while prediction and update of particles are performed in a synchronization region, thus our approach achieves realtime performance of 60 fps for VGA images, synchronized with the camera pixel throughput without using any external memory devices. Through evaluation with an object tracking benchmark video, the tradeoff relationship between tracking quality and the number of particles is analyzed to find an appropriate hardware parameters. In addition, we address improvement of resource utilization for our particle filter architecture, especially by using a higher clock frequency to reuse hardware resources in a time sharing manner. The implementation experiments reveal that the proposed approach allows the original design to be fitted in a smaller FPGA chip. However, we also demonstrate this size reduction approach has an overhead of 2.7 to 3.0 times power consumption compared to original designs with a slow clock frequency.