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MULTI-TARGET TRACKING BY DISCRIMINATIVE ANALYSIS ON RIEMANNIAN MANIFOLD

Abstract : This paper addresses the problem of multi-target tracking in crowded scenes from a single camera. We propose an algorithm for learning discriminative appearance models for different targets. These appearance models are based on covariance descriptor extracted from tracklets given by a short-term tracking algorithm. Short-term tracking relies on object descriptors tuned by a controller which copes with context variation over time. We link tracklets by using discriminative analysis on a Riemannian manifold. Our evaluation shows that by applying this discriminative analysis, we can reduce false alarms and identity switches, not only for tracking in a single camera but also for matching object appearances between non-overlapping cameras.
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https://hal.inria.fr/hal-00703633
Contributor : Slawomir Bak <>
Submitted on : Thursday, June 7, 2012 - 1:52:57 PM
Last modification on : Thursday, March 5, 2020 - 5:34:24 PM
Long-term archiving on: : Monday, September 10, 2012 - 11:25:36 AM

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Slawomir Bak, Duc Phu Chau, Julien Badie, Etienne Corvee, François Bremond, et al.. MULTI-TARGET TRACKING BY DISCRIMINATIVE ANALYSIS ON RIEMANNIAN MANIFOLD. ICIP - International Conference on Image Processing - 2012, Sep 2012, Orlando, United States. pp.1-4. ⟨hal-00703633⟩

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