Kim Meng Liang

Hybrid blob and particle filter tracking approach for robust object tracking

Abstract 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.

GuARD: A Real-Time System for Detecting Aggressive Human Behavior in Cage Environment

The relative closeness in a cage environment, such as lock-up or elevator, will become a place that is conducive to conduct criminal activities such as fighting. Monitoring the activities, in the cage environment, therefore, became a necessity. However, placing security guards could be inefficient and ineffective, as it is impossible to monitor the scene 24 by 7. A vision-based system, employing video analysis technology, to detect abnormalities such as aggressive behavior, becomes a challenging and emerging problem. In order to monitor suspicious activities in a cage environment, the system should be able track individuals from the scene, to identify their action, and to keep a record of how often these aggressive behaviors happen. On top of the previous consideration, the system should be implemented in real-time, whereby, the following conditions were taken into consideration, being: (1) wide angle (fish-eye) (2) resolution (low) (3) number of people (4) lighting (low). This paper proposes to develop a vision-based system that is able to monitor aggressive activities of individuals in a cage environment. This work focuses on analyzing the temporal feature of aggressive movement, taking consideration of the acquisition limitations discusses previously. Experimental results show that the proposed system is easily realized and achieved real-time performance, even in low performance computer.

Ghost removal for background subtraction using color similarity comparison

This paper aims to solve the problem of detecting ghost object; which is a common problem in background subtraction algorithm. Ghost object is the false object detected which is not corresponding to any actual object in current image. In this work, we proposed ghost detection and removal method using color similarity comparison. Proposed solution is designed based on the assumption that ghost problem occurs due to the existence of the object in background image instead of in the current image. We are using color similarity between detected foreground area and its surrounding area to first determine whether the object appear in background or current image, consequently identify whether the detected object is a ghost or an actual object. Proposed solution has been tested using various datasets including PETS2001 and own datasets and it is proved that the proposed method is able to solve ghost problem.