Hock Woon Hon

Real time intrusion detection system for outdoor environment

This paper aims to present the overall architecture of intrusion detection system and its effectiveness in detecting intruder at an outdoor environment. The outdoor environment is exposing to various brightness condition in terms of the weather, day transition, dynamic background of the scene, shadow and the visibility degree of the infrared camera. In this work, the capturing devices and intrusion detection system are setup physically at the pilot site. A thorough evaluation is done for consecutive three days in order to measure the accuracy of the system in detecting intruder throughout the day and night. In addition, the system is evaluated together with a commercial intrusion detection system from Australia as a technology benchmark. The accuracy of the proposed system and benchmark system is illustrated in this paper and the proposed system delivers higher accuracy as compared to the benchmark system.

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.

Virtual Fence for a Surveillance System

This paper presents a method for defining one or more virtual restricted zones within a surveillance area which is observed with stereo cameras. When an object enters a restricted zone, the system highlights the object shown in the monitoring screen or triggers other devices to produce a visual or auditory alarm. The proposed method works by extracting the foreground objects for both the left and the right images from their respective stereo cameras. Then it estimates the objects position in terms of depth plane using image shifting and number of overlapping pixels. Finally, it determines whether there is a collision between objects and restricted zones in order to trigger an alarm where necessary. The algorithm has been tested with a series of stereo videos, in which samples of it are presented in this paper.