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Face-TLD: Tracking-learning-detection applied to faces

Kalal, Z, Mikolajczyk, K and Matas, J (2010) Face-TLD: Tracking-learning-detection applied to faces Proceedings - International Conference on Image Processing, ICIP. pp. 3789-3792.

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Abstract

A novel system for long-term tracking of a human face in unconstrained videos is built on Tracking-Learning-Detection (TLD) approach. The system extends TLD with the concept of a generic detector and a validator which is designed for real-time face tracking resistent to occlusions and appearance changes. The off-line trained detector localizes frontal faces and the online trained validator decides which faces correspond to the tracked subject. Several strategies for building the validator during tracking are quantitatively evaluated. The system is validated on a sitcom episode (23 min.) and a surveillance (8 min.) video. In both cases the system detectstracks the face and automatically learns a multi-view model from a single frontal example and an unlabeled video

Item Type: Article
Authors :
NameEmailORCID
Kalal, ZUNSPECIFIEDUNSPECIFIED
Mikolajczyk, KUNSPECIFIEDUNSPECIFIED
Matas, JUNSPECIFIEDUNSPECIFIED
Date : 2010
Identification Number : 10.1109/ICIP.2010.5653525
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 13:12
Last Modified : 31 Oct 2017 16:59
URI: http://epubs.surrey.ac.uk/id/eprint/806158

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