Visual tracking of multiple targets is a key step in surveil-lance scenarios, far from being solved due to its intrinsicill-posed nature. In this paper, a comparison of Multi-Hypothesis Kalman Filter and Particle Filter-based track-ing is presented. Both methods receive input from a novelonline background subtraction algorithm. The aim of thiswork is to highlight advantages and disadvantages of suchtracking techniques. Results are performed using publicchallenging data set (PETS 2009), in order to evaluate theapproaches on significant benchmark data.
A Comparison of Multi Hypothesis Kalman Filter and Particle Filter for Multi-target Tracking
D. Bloisi;
2009-01-01
Abstract
Visual tracking of multiple targets is a key step in surveil-lance scenarios, far from being solved due to its intrinsicill-posed nature. In this paper, a comparison of Multi-Hypothesis Kalman Filter and Particle Filter-based track-ing is presented. Both methods receive input from a novelonline background subtraction algorithm. The aim of thiswork is to highlight advantages and disadvantages of suchtracking techniques. Results are performed using publicchallenging data set (PETS 2009), in order to evaluate theapproaches on significant benchmark data.File in questo prodotto:
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