A novel object position coding for multi-object tracking using sparse representation
| Autor: | Nasreddine Taleb, Kidiyo Kpalma, Mohamed Elbahri, Miloud Chikr El-Mezouar |
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| Přispěvatelé: | Université Djilali Liabès [Sidi-Bel-Abbès], Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Nantes Université (NU)-Université de Rennes 1 (UR1) |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Background subtraction
K-SVD business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Sparse approximation Classification Matching pursuit Silhouette Computer Science::Computer Vision and Pattern Recognition Histogram Video tracking Multi-object tracking Function representation Computer vision Artificial intelligence Object representation Orthogonal matching pursuit business Sparse representation [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Mathematics |
| Zdroj: | International Journal of Image, Graphics and Signal Processing International Journal of Image, Graphics and Signal Processing, 2015, 11 p International Journal of Image, Graphics and Signal Processing, Modern Education and Computer Science Press(MECS Press), 2015, 11 p |
| ISSN: | 2074-9074 2074-9082 |
| Popis: | 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. Index Terms—Multi-object tracking, Object representation, Orthogonal matching pursuit, Sparse representation, Classification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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