Implementation of GPU-based Dynamic Object Tracking Algorithm

• Main Authors: Po-Hsuan Su, 蘇柏亘
• Other Authors: Chih-Yuan Yao
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/74437342025524901278

碩士 === 國立臺灣科技大學 === 資訊工程系 === 104 === As mobile cameras become widely available to the mass, the amount of video data created with them has reached a point where manually editing each video is impractical. In that wake, many algorithms for automatic object detection, segmentation and tracking were proposed. As of now, the fundamental algorithm for object tracking uses the difference between current scene and static scene to detect object. However, the algorithm can fail due to the difference in lighting on the two scene. And to resolve the problem would require a massive computation time,resulting in poor performance of the algorithm on low-end hardware. In this paper, we propose an algorithm that can detect the boundary of a dynamic object with a single camera. We utilized graphics processing units to improve the algorithm's performance and achieved real-time efficiency on low-end developer hardware. Our sample model is initialized with a single image and constantly updated with new images of current scene during execution to successfully track dynamic objects. And finally, we detect the bounding box of segmented objects with connected component labeling. The performance of dynamic object detection on IGS's developer hardware without GPU acceleration is 14FPS. After GPU acceleration the performance is improved to 32.6FPS, and 27FPS when boundary calculation is included.