A hierarchical structure with improved OMP sparse representation used with face recognition

Zhang, Jian; Zhang, Hongzhi; Li, Zhengming

doi:10.1016/j.ijleo.2014.05.018

Cited by 3 publications

(1 citation statement)

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“…Our implementation consists of an OMP used to find the sparse coefficients. This algorithm was chosen for its speed of convergence due to the orthogonal projection [42,43]. In [15], Weizhi Lu et al have shown that even when the dictionary has some false training samples, the sparse coefficients can be correctly grouped using the OMP algorithm.…”

Section: B Omp Algorithmmentioning

confidence: 99%

A Novel Object Position Coding for Multi-Object Tracking using Sparse Representation

Elbahri¹,

Kpalma²,

Taleb³

et al. 2015

IJIGSP

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Abstract-Multi-object tracking is a challenging task, especially when the persistence of the identity of objects is required. In this paper, we propose an approach based on the detection and the recognition. To detect the moving objects, a background subtraction is employed. To solve the recognition problem, a classification system based on sparse representation is used. With an online dictionary learning, each detected object is classified according to the obtained sparse solution. Each column of the used dictionary contains a descriptor representing an object. Our main contribution is the representation of the moving object with a descriptor derived from a novel representation of its 2-D position and a histogram-based feature, improved by using the silhouette of this object. Experimental results show that the approach proposed for describing moving objects, combined with the classification system based on sparse representation provides a robust multi-object tracker in videos involving occlusions and illumination changes.

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Section: B Omp Algorithmmentioning

confidence: 99%