Abstract
In recent years, correlation filter based trackers have seen widespread success because of their high efficiency and robustness. However, a single feature based tracker cannot deal with complex scenes such as serious occlusion, motion blur and illumination variation. In this paper, we develop a novel tracking method combining color feature, Hog feature and motion feature. The motion feature is estimated between adjacent frames by large displacement optical flow. Besides, in order to cope with boundary effect existing in traditional correlation filter based trackers, an adaptive cosine window is introduced in our method, which can highlight the target region, suppress the background region and enlarge search region. Meanwhile, a novel judge scheme combining Hog correlation response and color response is adopted to evaluate the reliability of tracking result. Finally, inverse sparse representation is presented to locate coarse positions of target in case of tracking failures. Extensive experiments on five famous tracking benchmarks including OTB100, TColor-128, UAVDT, UAV123 and VOT2016 demonstrate our proposed method outperform other sate-of-the-art methods in terms of robustness and accuracy.
Highlights
Visual object tracking, one of the classical and fundamental research topics in computer vision, has long been widely used in traffic monitoring, medical image processing, automatic driving and video surveillance
The tracking performance of our method under low resolution has been improved by 2.2% as compared with the baseline RLT tracker
Motion feature is estimated by large displacement optical flow through adjacent frames, which is combined with color response and Hog correlation response to promote the tracking performance significantly
Summary
One of the classical and fundamental research topics in computer vision, has long been widely used in traffic monitoring, medical image processing, automatic driving and video surveillance. Great breakthrough has been made in the past decade [1]–[13], designing a general and robust tracker remains a challenging task, due to many unpredictable factors including illumination change, scale variation, serious occlusion, motion blur, and so on. Trackers based on correlation filter (CF) have been proposed and obtained promising performance on many challenging benchmarks. The core of CF trackers is to train a discriminative classifier to separate the target from its surrounding background [14]–[16]. Discriminative correlation classifiers are trained with the circulant shifted version of the target and only the detection scores near the center of searching region are accurate. Only a restricted search area is used to train the correlation filter, which makes CF trackers drift to the background in the presence of heavy occlusion and motion blur.
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