Abstract
The discriminative correlation filters-based methods struggle deal with the problem of fast motion and heavy occlusion, the problem can severely degrade the performance of trackers, ultimately leading to tracking failures. In this paper, a novel Motion-Aware Correlation Filters (MACF) framework is proposed for online visual object tracking, where a motion-aware strategy based on joint instantaneous motion estimation Kalman filters is integrated into the Discriminative Correlation Filters (DCFs). The proposed motion-aware strategy is used to predict the possible region and scale of the target in the current frame by utilizing the previous estimated 3D motion information. Obviously, this strategy can prevent model drift caused by fast motion. On the base of the predicted region and scale, the MACF detects the position and scale of the target by using the DCFs-based method in the current frame. Furthermore, an adaptive model updating strategy is proposed to address the problem of corrupted models caused by occlusions, where the learning rate is determined by the confidence of the response map. The extensive experiments on popular Object Tracking Benchmark OTB-100, OTB-50 and unmanned aerial vehicles (UAV) video have demonstrated that the proposed MACF tracker performs better than most of the state-of-the-art trackers and achieves a high real-time performance. In addition, the proposed approach can be integrated easily and flexibly into other visual tracking algorithms.
Highlights
Visual object tracking is one of the most popular fields in computer vision for its wide applications including unmanned vehicles, video surveillance, unmanned aerial vehicles (UAV), and human-computer interaction, where the goal is to estimate the locus of the object given only by an initial bounding box from the first frame in the video stream [1]
Qualitative and quantitative experiments on OTB-50, OTB-100 and UAV video have demonstrated that our approach outperforms most of the state-of-the-art trackers
The comprehensive experiments have been tested on the popular benchmark OTB-50, OTB-100 and UAV video, and the results have demonstrated that our Motion-Aware Correlation Filters (MACF) approach surpasses most of the state-of-the-art methods
Summary
Visual object tracking is one of the most popular fields in computer vision for its wide applications including unmanned vehicles, video surveillance, UAV, and human-computer interaction, where the goal is to estimate the locus of the object given only by an initial bounding box from the first frame in the video stream [1]. ASMS is a real-time algorithm using the color histogram features for visual tracking where a scale estimation strategy is added to the classical mean-shift framework. It is distracted by similar objects in the surroundings. The discriminative approaches which are called as ‘track-by-detection methods’ are popular for their high accuracy, robustness, and real-time performance These methods employ machine-learning techniques to train classifiers by numbers of positive and negative samples extracted from the previous frame, and use the trained classifiers to find the optimal area of the target and locate the position of the target. The Discriminative Correlation Filter-based (DCF-based) approach is one of the most popular approach
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