Mean-shift human tracking based on combination of ICA and color features on active camera.

• Main Authors: Liu, Che-Nan, 劉哲男
• Other Authors: Lin, Chin-Teng
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/92070668128681425667

碩士 === 國立交通大學 === 電控工程研究所 === 98 === In recent years, detection and tracking are important tasks in computer vision for visual-based surveillance system. Visual-based surveillance system is a widespread application in parking, patient monitoring and security surveillance fields. In this thesis, we use human detection and tracking algorithm based on active camera. In the past, active camera based object tracking used temporal difference to find object position and then drive pan or tilt command to control the active camera. Although this process can achieve moving object tracking. However, to find moving object position, the active camera should be stopped for the computation of temporal difference. Therefore, the active camera can not pan/tilt continuously and smoothly. In the other words, if the active camera is able to keep moving the whole time, we will capture blur images, and temporal difference will extract not only moving object but also background. Therefore, it is impossible to accurately locate the position of moving object by using temporal difference while the active camera is moving. So we propose mean-shift human tracking based on combination of ICA and color feature on active camera to solve above problem. This thesis consists of three major parts: Human detection, human tracking and pan/tilt control. In human detection system, the independent component analysis (ICA) and support machine vector (SVM) classifier are applied to classify moving objects into human or non-human. When a human is detected then we need to track it. Mean-shift algorithm will track the target by computing the similarity value in every frame and send the position to active camera, and then active camera will drive PTZ to keep the target in the center of FOV (field of view). Sometimes human will be partially or fully occluded by other object, thus the similarity will drastically decrease. Consequently, mean-shift will miss the target. To overcome the above problem, the Kalman filter is applied to predict the target’s position in next frame. The mean-shift using only color feature will miss the tracking target, when the target object and background have the same color. In order to solve the missing problem, we propose a novel mean-shift algorithm based on combination of ICA and color feature. Since the ICAs modeled human characteristic with gray-level training data, the proposed algorithm can distinguish human and background with the same color.