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This paper presents a new sensor management method for multitarget filtering, that is designed based on maximizing a measure of confidence in accuracy of the multitarget state estimate. Confidence of estimation is quantified by optimal subpattern assignment-based dispersion of the multitarget posterior about its statistical mean. Implementation of the algorithm for generic multitarget filters is presented...
This paper presents a novel solution to the occlusion handling problem in pedestrian tracking using labeled random finite set theory. The occlusion handling module uses motion and color cues of tracked targets to recover target labels after occlusion. An effective algorithm is also proposed for false alarm detection and removal which is designed based on tracked targets features such as, overlap ratio,...
This paper presents a labeled multi-Bernoulli filter for track-before-detect with a special focus on visual tracking of multiple targets in video. We show that labeled multi-Bernoulli distribution is a conjugate prior for an image likelihood function with a specific separable form. Following a previously formulated likelihood function (with the desirable separable form) using background subtraction,...
A new approach to solve the sensor control problem is proposed, formulated based on multi-object Bayes filtering in the partially observable Markov decision process (POMDP) context, where the multi-object states are assumed to be random finite sets with multi-Bernoulli distributions. We introduce a novel cost function that is reliable in real-time environment. In each filtering iteration, after predicting...
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