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Multi-Target Tracking by On-Line Learned Discriminative Appearance Models

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Multi-Target Tracking by On-Line Learned Discriminative Appearance Models
We present an approach for online learning of discriminative appearance models for robust multi-target tracking in a crowded scene from a single camera. Although much progress has been made in developing methods for optimal data association, there has been comparatively less work on the appearance models, which are key elements for good performance. Many previous methods either use simple features such as color histograms, or focus on the discriminability between a target and the background which does not resolve ambiguities between the different targets. We propose an algorithm for learning a discriminative appearance model for different targets. Training samples are collected online from tracklets within a time sliding window based on some spatial-temporal constraints; this allows the models to adapt to target instances. Learning uses an AdaBoost algorithm that combines effective image descriptors and their corresponding similarity measurements. We term the learned models as OLDAMs....
Cheng-Hao Kuo, Chang Huang, Ram Nevatia
Added 23 Jun 2010
Updated 23 Jun 2010
Type Conference
Year 2010
Where CVPR
Authors Cheng-Hao Kuo, Chang Huang, Ram Nevatia
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