Particle PHD Filtering for Multi-Target Visual Tracking

We propose a multi-target tracking algorithm based on the Probability Hypothesis Density (PHD) filter and data association using graph matching. The PHD filter is used to compensate for miss-detections and to remove noise and clutter. This filter propagates the first order moment of the multi-target posterior (instead of the full posterior) to reduce the growth in complexity with the number of targets from exponential to linear. Next the filtered states are associated using graph matching. Experimental results on face, people and vehicle tracking show that the proposed multi-target tracking algorithm improves the accuracy of the tracker, especially in cluttered scenes.

• Type: Conference
• Year: 2007
• Where: ICASSP
• Authors: Emilio Maggio, Elisa Piccardo, Carlo S. Regazzoni, Andrea Cavallaro
• Keywords: ICASSP 2007, Multi-target Tracking Algorithm, Probability Hypothesis Density, Signal Processing, first Order Moment