Multiple Visual Targets Tracking Via Probability Hypothesis Density Filter and Feature Measurement

作者：Lu, Xiaofeng; Izumi, Takashi^*; Teng, Lin; Wang, Lei

来源：IEEJ Transactions on Electrical and Electronic Engineering, 2014, 9(6): 685-691.

DOI：10.1002/tee.22026

• probability hypothesis density (PHD) filter
• feature measurement
• visual tracking
• Monte Carlo method

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multiple-targets filter based on random finite sets (RFS). It propagates the posterior intensity of the random sets of targets. In this paper, we apply the PHD filter to track a random number of moving targets in visual sequences. The PHD filter is implemented using a Gaussian mixture. Obtaining the PHD only for one visual frame at a time remains a challenge. To meet this challenge, we propose a method to approximate the posterior intensity using feature measurement. To improve the representability of tracking target, we adopt an adaptive weight to fuse the color and local binary pattern (LBP) features which are extracted by the Monte Carlo method. Experimental results demonstrate the effectiveness of our method.

• 出版日期2014-11
• 单位西安理工大学