An extended sparse classification framework for domain adaptation in video surveillance

Nourbakhsh, Farshad, Granger, Eric et Fumera, Giorgio. 2017. « An extended sparse classification framework for domain adaptation in video surveillance ». In Computer Vision – ACCV 2016 Workshops : ACCV 2016 International Workshops, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III (Taipei, Taiwan, Nov. 20-24, 2016) Coll. « Lecture Notes in Computer Science », vol. 10118. pp. 360-376. Springer Verlag.
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Résumé

Still-to-video face recognition (FR) systems used in video surveillance applications capture facial trajectories across a network of distributed video cameras and compare them against stored distributed facial models. Currently, the performance of state-of-the-art systems is severely affected by changes in facial appearance caused by variations in, e.g., pose, illumination and scale in different camera viewpoints. More- over, since an individual is typically enrolled using one or few reference stills captured during enrolment, face models are not robust to intra-class variation. In this paper, the Extended Sparse Representation Classiffication through Domain Adaptation (ESRC-DA) algorithm is proposed to improve performance of still-to-video FR. The system's facial mod- els are thereby enhanced by integrating variational information from its operational domain. In particular, robustness to intra-class variations is improved by exploiting: (1) an under-sampled dictionary from tar- get reference facial stills captured under controlled conditions; and (2) an auxiliary dictionary from an abundance of unlabelled facial trajecto- ries captured under different conditions, from each camera viewpoint in the surveillance network. Accuracy and effciency of the proposed tech- nique is compared to state-of-the-art still-to-video FR techniques using videos from the Chokepoint and COX-S2V databases. Results indicate that ESRC-DA with dictionary learning of unlabelled trajectories provides the highest level of accuracy, while maintaining a low complexity.

Type de document:	Compte rendu de conférence
ISBN:	03029743
Professeur:	Professeur Granger, Éric
Affiliation:	Génie de la production automatisée
Date de dépôt:	11 avr. 2017 14:19
Dernière modification:	28 janv. 2020 16:17
URI:	https://espace2.etsmtl.ca/id/eprint/15044

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