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Title: Deep location-specific tracking
Authors: Yang, L 
Liu, R
Zhang, D 
Zhang, L 
Keywords: Convolutional neural networks
Location specific tracking
Single object tracking
Visual tracking
Issue Date: 2017
Publisher: Association for Computing Machinery, Inc
Source: MM 2017 - Proceedings of the 2017 ACM Multimedia Conference, 2017, p. 1309-1317 How to cite?
Abstract: Convolutional Neural Network (CNN) based methods have shown significant performance gains in the problem of visual tracking in recent years. Due to many uncertain changes of objects online, such as abrupt motion, background clutter and large deformation, the visual tracking is still a challenging task. We propose a novel algorithm, namely Deep Location-Specific Tracking, which decomposes the tracking problem into a localization task and a classification task, and trains an individual network for each task. The localization network exploits the information in the current frame and provides a specific location to improve the probability of successful tracking, while the classification network finds the target among many examples generated around the target location in the previous frame, as well as the one estimated from the localization network in the current frame. CNN based trackers often have massive number of trainable parameters, and are prone to over-fitting to some particular object states, leading to less precision or tracking drift. We address this problem by learning a classification network based on 1 × 1 convolution and global average pooling. Extensive experimental results on popular benchmark datasets show that the proposed tracker achieves competitive results without using additional tracking videos for fine-tuning.
Description: 25th ACM International Conference on Multimedia, MM 2017, Mountain View, CA, USA, 23-27 October, 2017
ISBN: 9781450349062
DOI: 10.1145/3123266.3123381
Appears in Collections:Conference Paper

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