An intelligent paradigm for multi-objects tracking in crowded environment

In this paper, we describe a novel object tracking technique in color video sequences, with application to multi-object tracking in crowded scenes. The proposed paradigm integrates object detection into the object tracking process and provide a robust tracking framework under ambiguity conditions. In order to reduce the computational complexity and to increase the robustness, we use a tri-sectional structure. i.e., firstly it distinguishes between real world objects, secondly extracts image features like motion blobs and color patches and thirdly abstracts objects like meta-objects that shall denote real world objects. Through such a tight integration of the motion blobs and color patches, as well as the global optimization of object trajectories, we have accomplished not only robust and efficient multi-object tracking, but also the ability to deal with merging/splitting of objects, irregular object motions, changing appearances, etc. which are the challenging problems for the most traditional tracking methods. The efficiency of the suggested technique for multi-objects detection and tracking will be demonstrated in this paper on the basis of analysis of strongly disturbed real image sequences.