Segmentation-tracking feedback approach for high-performance video surveillance applications

Cuevas, Carlos; del-Blanco, Carlos R.; García, Narciso; Jaureguizar, Fernando

doi:10.1109/ssiai.2010.5483922

Cited by 1 publication

(1 citation statement)

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“…This fact can make them inappropriate for online applications. Some works [15][16][17] overcome this limitation by designing an efficient and non-iterative proposal distribution that depends on the specific characteristics of the tracking system. An additional problem is that the accuracy of the estimated object trajectories can be very poor due to the high dimensionality of the tracking problem.…”

Section: State Of the Artmentioning

confidence: 99%

An advanced Bayesian model for the visual tracking of multiple interacting objects

del-Blanco

Jaureguizar

García

2011

EURASIP J. Adv. Signal Process.

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Visual tracking of multiple objects is a key component of many visual-based systems. While there are reliable algorithms for tracking a single object in constrained scenarios, the object tracking is still a challenge in uncontrolled situations involving multiple interacting objects that have a complex dynamics. In this article, a novel Bayesian model for tracking multiple interacting objects in unrestricted situations is proposed. This is accomplished by means of an advanced object dynamic model that predicts possible interactive behaviors, which in turn depend on the inference of potential events of object occlusion. The proposed tracking model can also handle false and missing detections that are typical from visual object detectors operating in uncontrolled scenarios. On the other hand, a Rao-Blackwellization technique has been used to improve the accuracy of the estimated object trajectories, which is a fundamental aspect in the tracking of multiple objects due to its high dimensionality. Excellent results have been obtained using a publicly available database, proving the efficiency of the proposed approach.

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Section: State Of the Artmentioning

confidence: 99%