We describe a system for detection and classification of moving targets. The system's change detection and tracking modules are based on background adaptation, with the help of information about targets obtained from preceding time steps. The classification module performs a hybrid classification that combines motion and appearance features. The system is able to perform real-time detection, tracking and classification of targets in outdoor settings. Experiments demonstrate that the proposed hybrid classifier architecture improves classification significantly, thereby permitting real-time discrimination among a considerable number of classes, some of which are quite similar.
We provide an extended formulation of size O(log n) ⌊ d 2 ⌋ for the cyclic polytope with dimension d and n vertices (i, i 2 , . . . , i d ), i ∈ [n]. First, we find an extended formulation of size log(n) for d = 2. Then, we use this as base case to construct small-rank nonnegative factorizations of the slack matrices of higher-dimensional cyclic polytopes, by iterated tensor products. Through Yannakakis's factorization theorem, these factorizations yield smallsize extended formulations for cyclic polytopes of dimension d ≥ 3.S. Fiorini was partially funded by F.R.S.-FNRS (scientific mission OUT).
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