Abstract
The core part of the popular tracking-by-detection trackers is the discriminative classifier, which distinguishes the tracked target from the surrounding environment. Correlation filter-based visual tracking methods have the advantage of computing efficiency over the traditional methods by exploiting the properties of circulant matrix in learning process, and the significant progress in efficiency has been achieved by making use of the fast Fourier transform at detection and learning stages. But most existing correlation filter-based approaches are mainly restricted to translation estimation, which are susceptible to drifting in long-term tracking. In this article, a compressed multiple feature and adaptive scale estimation method is presented, which uses multiple features, including histogram of orientation gradients, color-naming, and raw pixel value to further improve the stability and accuracy of translation estimation. And for the scale estimation, another correlation filter is trained, which uses the compressed histogram of orientation gradients and raw pixel value to construct a multiscale pyramid of the target, and the optimal scale is obtained by exhaustively searching. The translation and scale estimation are unified with an iterative searching strategy. Extensively experimental results on the benchmark data set of scale variation show that the performance of the proposed compressed multiple feature and adaptive scale estimation algorithm is competitive against state-of-the-art methods with scale estimation capabilities in terms of robustness and accuracy.
Highlights
Visual object tracking is one of the fundamental problems of computer vision and widely used in many applications, such as driverless vehicle, intelligent human–computer interaction, security, video surveillance and analysis, video encoding, augmented reality, traffic control in intelligent transportation system, video editing,[1] and so on
There are some challenging factors for visual tracking, such as appearance changing, scale variations, occlusions, motion blur, and fast motion, some of these factors come from the motion between the object and camera, some come from the target itself, such as geometric deformations, and some
We address the problems that the target undergoes large appearance changing mainly caused by the relative motion between the camera and target, or the deformation of target and heavy occlusion, and so on, in longterm visual tracking with Correlation filter (CF)
Summary
Visual object tracking is one of the fundamental problems of computer vision and widely used in many applications, such as driverless vehicle, intelligent human–computer interaction, security, video surveillance and analysis, video encoding, augmented reality, traffic control in intelligent transportation system, video editing,[1] and so on. It forms a basic part of higher level vision tasks such as scene analysis and behavior recognition. Discriminative model-based methods usually use the binary classifier or learning-based techniques to recognize the tracked object from the background. The discriminative learning methods made a big progress in visual tracking research recently
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
