Abstract
Target tracking is an important area of research in computer vision where stable target's tracking has been well solved. But in real world, it is difficult to ensure that the camera or lens could be fixed and the target could maintain its shape in whole video sequence. And as a result, in these unstable cases, robust tracking algorithms have to deal with the problem of target shape-deforming. Once the scenes video sequence contains shape-deformed target, tracking become a real challenging problem. Most previous tracking algorithms based on craft features only used HOG or/and CN features. This paper proposed an algorithm named as Correlation Filtering with Motion Detection (CFMD). This algorithm takes into account the camera shake and target motion information of the video sequence. After removing the effects of lens shake and camera movement, this algorithm can predict the motion information of the target, thereby effectively improving the tracking accuracy and robustness. In CFMD, the target position is determined by the weighted outputs of motion detection and correlation filter tracker. We evaluated our CMFD algorithm on the OTB-100 and VOT-2018 dataset compared with other target tracking algorithms, including Kernel Correlation Filter (KCF), Scale Adaptive with Multiple Features tracker (SAMF), Discriminative Scale Space Tracker (DSST), and Sum of Template and Pixel-wise LEarners (Staple), Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking(STRCF), Multi-Cue Correlation Filters for Robust Visual Tracking(MCCT). The experimental results showed that our algorithm owns the property of robust tracking of shape-deformed targets in video sequences containing lens shaking or camera moving and it achieves the state-of-the-art precision and tracking effects.
Highlights
Target tracking, which estimates the position of a target object in a video sequence, remains an important area of research in computer vision and is widely used in many fields, such as machine perception, video compression, human–computer interaction, etc
We compared the proposed method (CFMD) with the state-of-the-art algorithms based on correlation filtering, including Kernel Correlation Filter (KCF) [11], Scale Adaptive with Multiple Features tracker (SAMF) [13], Discriminative Scale Space Tracker (DSST) [14], Sum of Template and Pixel-wise LEarners (Staple) [17], STRCF [21], MCCT [22], BACF [34] and ECO [35]
Our Correlation Filtering with Motion Detection (CFMD) algorithm can obtain much better tracking precision in the video sequences containing shape-deformed targets. These video sequences contain lens shaking, camera motion, or object shape changing, i.e. these four one have deformed shape and it is a real challenge for KCF, SAMF, DSST and Staple
Summary
Target tracking, which estimates the position of a target object in a video sequence, remains an important area of research in computer vision and is widely used in many fields, such as machine perception, video compression, human–computer interaction, etc. Based on the idea of classification [6], the model framework uses the classifier learning method to distinguish background and target, such as TLD tracker [7], [8], L1APG algorithm [9] and Correlation Filter(CF) tracker [10]. As the best representative of tracking algorithms using CNN or deep structure, the Siamese trackers formulate the visual object tracking problem as to learn a general similarity map by cross-correlation between the feature representations from the target template and the search [23]. 3. The proposed algorithm can deal with more difficult tracking tasks than existing ones, especially when the target deforms greatly with fast motion, the lens shakes severely or the camera moves rapidly
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