ACM Computing Surveys (CSUR)
Hardware-assisted multiple object tracking for human-robot-interaction
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
A Bayesian Approach to Multiple Target Detection and Tracking
IEEE Transactions on Signal Processing
Multiple Object Tracking Via Species-Based Particle Swarm Optimization
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we present the design and implementation of real-time multiple object centroid tracking for gesture recognition. Our multiple object tracking design consists of four stages: preprocessing, local intensity accumulation, object observation, and particle filter. We implemented the proposed hardware architecture using Verilog Hardware Description Language (HDL) on a Xilinx Virtex-5 LX330 field programmable gate array (FPGA). We focus on two main performances: the trajectory accuracy of moving objects and real-time processing. The performance of the proposed system was evaluated through several experiments. In addition, our processing speed was compared with the same algorithm based on software. Based on the results, we can guarantee that our multiple object tracking design is suitable for gesture recognition in cluttered environments.