Model-driven multi-target tracking in crowd scenes
Dongyan Liu, Zhipei Huang, Jiankang Wu · International Conference on Information Fusion · 2013
Multi-target tracking in crowd scenes is a highly challenging problem due to appearance ambiguity and frequent occlusions between different targets. While many impressive works have been done on complex appearance models and data association framework, we address the importance of social behaviour knowledge to overcome these challenges. The proposed model, termed Crowd Context Model (CCM), offers a general framework which jointly models the appearance features and behaviour rules together, with cooperation methods to achieve model-driven multi-target tracking. We use behaviour modelling approach to make reasonable prediction on pedestrian's location. A Multi-template Appearance Model (MAM) using simple appearance features is adopted for target localization. Experiments on real video sequences show that the proposed model-driven method improves the performance of multi-target tracking successfully, especially during occlusions.