Inter-Camera Association of Multi-Target Tracks by On-line Learned Appearance Affinity Models

We propose a novel system for associating multi-target tracks across multiple non-overlapping cameras by an on-line learned discriminative appearance affinity model. Collecting reliable training samples is a major challenge in on-line learning since supervised correspondence is not available at runtime. To alleviate the inevitable ambiguities in these samples, Multiple Instance Learning (MIL) is applied to learn an appearance affinity model which effectively combines three complementary image descriptors and their corresponding similarity measurements. Based on the spatial-temporal information and the proposed appearance affinity model, we present an improved inter-camera track association framework to solve the "target handover" problem across cameras. Our evaluations indicate that our method have higher discrimination between different targets than previous methods.

• Type: Conference
• Year: 2010
• Where: ECCV
• Keywords: Appearance Affinity Model, Computer Vision, Discriminative Appearance Affinity, ECCV 2010, Multiple Non-overlapping Cameras