Real-Time Visual Detection and Tracking of Multiple Moving Objects Based on Particle Filter Techniques

• Main Authors: Xuan-qing Song, 宋炫慶
• Other Authors: Qin-Xiong Fan
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/86592932642455768347

碩士 === 國立臺灣科技大學 === 資訊工程系 === 93 === In the last decade, due to the popularization of video products and the rapid development of computer vision techniques, the detection and tracking methods for dynamic images have been widely applied in many kinds of fields, such as video surveillance, intelligent transportation, and parking area management systems. They can replace a lot of bored and time-wasting work, and avoid mannal mistakes caused by fatigue of human. On the effectiveness for a given period of time, these visual detection and tracking systems possess the ability of reporting sudden situations in real time, so that the whole time costs of such systems can be greatly reduced. In this thesis, the detection phase of our developed system consists of four parts: background generation, foreground detection, shadow elimination, and background maintenance. In the background generation part, the median method is used for constructing background images from the past N frames. In the foreground detection part, an extraction function is applied to indirectly perform differencing to obtain foreground images. In the shadow elimination part, a deterministic nonmodel-based method is adopted to remove shadows. As to the background maintenance part, a history map which records the number of times of the changes of corresponding pixels is employed to maintain background images. In the tracking phase of the system, this thesis exploits a particle filter to track moving objects. The color distribution of a moving object is chosen as its features represented by a color probability histogram. In order to raise the accuracy of tracking, the background information serves as the increase candidate weight of a moving object. The experimental results reveal that in general situations our system can achieve real-time processing and can obtain robust detection and tracking results for multiple moving objects.