Video object tracking based on improved gradient vector flow snake and intra-frame centroids tracking method |
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| Affiliation: | 1. Department of Electrical and Electronics Engineering, Bilecik Seyh Edebali University, Bilecik 11210, Turkey;2. Department of Electrical and Electronics Engineering, Anadolu University, Eskisehir 26555, Turkey;1. Department of Electrical Engineering, COMSATS Institute of Information Technology, Park Road, Chak Shahzad, 44000 Islamabad, Pakistan;2. Center for Advanced Studies in Telecommunications, COMSATS Institute of Information Technology, Park Road, Chak Shahzad, 44000 Islamabad, Pakistan;3. Department of Physics, COMSATS Institute of Information Technology, Park Road, Chak Shahzad, 44000 Islamabad, Pakistan;1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081, China;1. College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China;2. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China;3. School of Physics and Engineering, Qufu Normal University, Qufu, Shandong 273165, China |
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| Abstract: | Accurately tracking the video object in video sequence is a crucial stage for video object processing which has wide applications in different fields. In this paper, a novel video object tracking algorithm based on the improved gradient vector flow (GVF) snake model and intra-frame centroids tracking algorithm is proposed. Unlike traditional gradient vector flow snake, the improved gradient vector flow snake adopts anisotropic diffusion and a four directions edge operator to solve the blurry boundary and edge shifting problem. Then the improved gradient vector flow snake is employed to extract the object contour in each frame of the video sequence. To set the initial contour of the gradient vector flow snake automatically, we design an intra-frame centroids tracking algorithm. Splitting the original video sequence into segments, for each segment, the initial contours of first two frames are set by change detection based on t-distribution significance test. Then, utilizing the redundancy between the consecutive frames, the subsequent frames’ initial contours are obtained by intra-frame motion vectors. Experimental results with several test video sequences indicate the validity and accuracy of the video object tracking. |
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