Abstract
Occlusion is a challenging problem in visual tracking. Therefore, in recent years, many trackers have been explored to solve this problem, but most of them cannot track the target in real time because of the heavy computational cost. A spatio-temporal context (STC) tracker was proposed to accelerate the task by calculating context information in the Fourier domain, alleviating the performance in handling occlusion. In this paper, we take advantage of the high efficiency of the STC tracker and employ salient prior model information based on color distribution to improve the robustness. Furthermore, we exploit a scale pyramid for accurate scale estimation. In particular, a new high-confidence update strategy and a re-searching mechanism are used to avoid the model corruption and handle occlusion. Extensive experimental results demonstrate our algorithm outperforms several state-of-the-art algorithms on the OTB2015 dataset.
Highlights
Visual tracking is one of the fundamental tasks in computer vision, with a plethora of applications such as video surveillance, robotics, human-computer interaction, etc
We attempt to develop a reliable and real-time tracking method based on a milestone tracker known as spatio-temporal context (STC) [1]
The STC tracker has a similar working framework as the other correlation filter (CF)-based trackers which take advantage of Fast Fourier operations to achieve high running speeds, while unlike the other algorithms, the STC tracker formulates the spatial-temporal relationships between the object and its locally dense contexts into the Bayesian framework to model the statistical correlation between the features from the targets and their surrounding regions
Summary
Zishu Zhao 1,† , Yuqi Han 2,† , Tingfa Xu 1,3, *, Xiangmin Li 1 , Haiping Song 4 and Jiqiang Luo 1. Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, Beijing 100081, China. Received: 21 August 2017; Accepted: 27 September 2017; Published: 10 October 2017
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