Abstract
In recent years, trackers based on correlation filters have attracted more and more attention due to the impressive tracking accuracy and real-time performance. However, in real scenarios, the tracking results are often been interfered with by the occlusion, illumination variation, appearance variation and background clutter. In order to find a tracker with better tracking performances, this paper proposed a multi-information fusion correlation filter tracker, which uses channel and spatial reliabilities and time regularization information on samples for filter training, and which not only extends the target search areas but also has a stronger ability to track the targets with significant appearance variations. Thus, results from extensive experiments conducted on OTB100, VOT2016, TC128, and UAV123 data sets show that our tracker with only directional gradient histogram (HOG) and color name (CN) features, performs favorably against the state-of-the-art trackers in terms of tracking precision, tracking success rate, tracking accuracy, and A-R rank.
Highlights
The problem of target tracking has received a significant contributions in recent times due to the rapid developments of artificial intelligence technologies [1]–[3]
In order to solve the boundary effects caused by cyclically shifted samples used for correlation filters training as much as possible, and obtain better tracking performance for the targets with appearance variation and occlusion, in this paper, we proposed a multi-information fusion correlation filter tracker, in which the channel and spatial reliabilities and time regularization information of samples are used for correlation filter training, and the channel and spatial reliabilities are refer to the corresponding contents of CSR-discriminant correlation filters (DCF) [10]
EXPERIMENTAL ANALYSIS Experimental results on the OTB100 [12], VOT2016 [13], TC128 [52], and UAV123 [2] data sets, suggests that the proposed correlation filters tracker with spatial and channel reliabilities and time regularization can effectively solve the boundary effects by making full use of the spatial, channel and temporal information of samples
Summary
The problem of target tracking has received a significant contributions in recent times due to the rapid developments of artificial intelligence technologies [1]–[3]. Target tracking refers to the continuous search of the target position and scale in subsequent video frames, given the target position and scale in the first frame. Target tracking technology has made substantial progress as a result of improved computer hardware performances and the introduction of new target tracking algorithms. As numerous adverse factors occur in real scenarios, such as target occlusion, scale variation, illumination variation, background variation, and appearance variation, etc., it is still a major challenge for a tracker to achieve high-precision, high success rate, and reasonable robustness. Correlation filter trackers train classifiers by minimizing errors. By extracting the target information and correlating with correlation filters, a group of target-possible
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