Hybrid blob and particle filter tracking approach for robust object tracking

Analysing and characterising human behaviour is now receiving much attention from the visual surveillance research community. Generally, human behaviour recognition requires human to be detected and tracked so that the trajectory patterns of the human can be captured and analysed for further interpretation. Therefore, it is crucial for tracking algorithms to be fast and robust to partial and short-life occlusion. In addition, the detection of object-of-interest to be tracked should be automated, without the need for manual intervention. This paper thus proposes a tracking system targeted for real time surveillance applications that integrate blob and simplified particle filter tracking approaches so as to exploit the advantages of both approaches while minimizing their respective disadvantages. The blob approach acts as the main tracking and will invoke the simplified particle filter tracking in the event of blob merging or occlusion. In this paper, the proposed tracking method is tested using PETS 2009 sequences to illustrate the capability of solving occlusion and obstruction in the scene. The results show that the proposed system successfully tracks objects during and after occlusion with other objects or after obstructed by the background