Tracking by recognition using neural network
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AbstractVision-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.
Acceptance Date19/04/2018
All Author(s) ListZeng Zhiliang, Yu Ying Kin, Wong Kin Hong
Name of Conference19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018
Start Date of Conference27/06/2018
End Date of Conference29/06/2018
Place of ConferenceBusan
Country/Region of ConferenceSouth Korea
Proceedings TitleProceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018
Year2018
PublisherIEEE
Article number8441158
Pages297 - 301
ISBN978-153865889-5
LanguagesEnglish-United Kingdom

Last updated on 2020-17-09 at 01:07