Tracking by recognition using neural network
Other conference paper

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其它資訊
摘要Vision-based object tracking is a challenging problem. In the tracking process, the object is usually first recognized in a given image. Then a bounding box is used to describe the position of the target object. Normally, a vector [x, y, width, height] is adopted to represent the bounding box. Under this viewpoint, the tracking problem can be treated as a regression problem if we handle the image sequence frame by frame. Due to the recent advancement in machine learning, many researchers apply neural networks to solve the visual tracking problem. This greatly improves the accuracy of bounding box prediction. Actually, the neural network based approaches are more suitable for end-to-end systems. In this paper, we propose to train and use a single neural network to tackle the tracking task. With the cropped candidate image patch as the input to the network, the output is the bounding box that indicates the target position. In our network, we first have a mask map to identify the target. It is a binary image and is divided into two classes. The positive class denotes the foreground while the negative class denotes the background. The mask map is then used for the estimation of the bounding box vector. The task now becomes an image mapping problem. We have achieved a good balance between accuracy and computational efficiency. Our tracker can reach an average speed of 178 frames per second(fps) and a maximum of 334 fps in the OTB benchmark.
出版社接受日期19.04.2018
著者Zeng Zhiliang, Yu Ying Kin, Wong Kin Hong
會議名稱19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018
會議開始日27.06.2018
會議完結日29.06.2018
會議地點Busan
會議國家/地區韓國
會議論文集題名Proceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018
出版年份2018
出版社IEEE
文章號碼8441158
頁次297 - 301
國際標準書號978-153865889-5
語言英式英語

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