An implementation of distance measure for dynamic scene

Wei, Sai; Liu, Zhengyi; Chuanwen, Hu

doi:10.1109/icicip.2013.6568106

Cited by 1 publication

(1 citation statement)

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“…Integrating the object position in the descriptor will indeed contribute to improve the recognition. In several works [15,32,33], the position is encoded using the simple coordinates of the object for its representation; others use position to compute the distance between objects and the camera. In the present work, the position is encoded as a vector derived from the whole frame; this vector is concatenated with the vector representing the appearance feature.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%