Multiple pedestrians tracking by discriminative models

We present our work on multiple pedestrians tracking in a single camera and across multiple non-overlapping cameras. We propose an approach for online learning of discriminative appearance models for robust multi-target tracking in a crowded scene. Although much progress has been made in developing methods for optimal data association, there has been comparatively less work on the appearance model, which is the key element 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 do not resolve ambiguities between the different targets. We propose an algorithm for learning discriminative appearance models 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. Our evaluations indicate that OLDAMs have significantly higher discrimination between different targets than conventional holistic color histograms, and when integrated into a hierarchical association framework, they help improve the tracking accuracy, particularly reducing the false alarms and identity switches. ❧ Furthermore, we extend our approach to multiple non-overlapping cameras. Given the multi-target tracking results in each camera, we propose a framework to associate those tracks. 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 has higher discrimination between different targets than previous methods.