MULTI-TARGET TRACKING BY DISCRIMINATIVE ANALYSIS ON RIEMANNIAN MANIFOLD

Slawomir Bak (1) , Duc Phu Chau (1) , Julien Badie (1) , Etienne Corvee (1) , François Bremond (1) , Monique Thonnat (1)

1 STARS - Spatio-Temporal Activity Recognition Systems

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.

Domaines: Informatique; Vision par ordinateur et reconnaissance de formes [cs.CV]

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⟩