Learning multiple instance deep representation for objects tracking |
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| Affiliation: | 1. College of Computer and Information, Hohai University, Nanjing 211100, PR China;2. School of Information Engineering, Nanjing Audit University, Nanjing 211815, PR China;3. School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, PR China;1. Department of Electronic Engineering, National Taipei University of Technology, Taipei City 10608, Taiwan;2. Department of Communication Engineering, National Central University, Taoyuan City 320, Taiwan;1. Department of Computer Science, Memorial University of Newfoundland, St. John’s A1B3X5, Canada;2. School of Computer Science, University of Guelph, Guelph N1G2W1, Canada;3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G1H9, Canada;1. School of Architecture, South China University of Technology, Guangzhou 510641, China;2. Foreign Language Teaching Department, Guang Zhou Vocational School of Finance and Economics, Guang Zhou 510080, China;3. School of Financial Mathematics and Statistics, GuangDong University of Finance, Guangzhou 510521, China |
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| Abstract: | Object tracking has been widely used in various intelligent systems, such as pedestrian tracking, autonomous vehicles. To solve the problem that appearance changes and occlusion may lead to poor tracking performance, we propose a multiple instance learning (MIL) based method for object tracking. To achieve this task, we first manually label the first several frames of video stream in image level, which can indicate that whether a target object in the video stream. Then, we leverage a pre-trained convolutional neural network that has rich prior information to extract deep representation of target object. Since the location of the same object in adjacent frames is similar, we introduce a particle filter to predict the location of target object within a specific region. Comprehensive experiments have shown the effectiveness of our proposed method. |
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| Keywords: | Object tracking Convolutional networks Multiple Instance Learning |
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