Abstract
Real-time visual object tracking is an open problem in computer vision, with multiple applications in the industry, such as autonomous vehicles, human-machine interaction, intelligent cinematography, automated surveillance, and autonomous social navigation. The challenge of tracking a target of interest is critical to all of these applications. Recently, tracking algorithms that use siamese neural networks trained offline on large-scale datasets of image pairs have achieved the best performance exceeding real-time speed on multiple benchmarks. Results show that siamese approaches can be applied to enhance the tracking capabilities by learning deeper features of the object’s appearance. SiamMask utilized the power of siamese networks and supervised learning approaches to solve the problem of arbitrary object tracking in real-time speed. However, its practical applications are limited due to failures encountered during testing. In order to improve the robustness of the tracker and make it applicable for the intended real-world application, two improvements have been incorporated, each addressing a different aspect of the tracking task. The first one is a data augmentation strategy to consider both motion-blur and low-resolution during training. It aims to increase the robustness of the tracker against a motion-blurred and low-resolution frames during inference. The second improvement is a target template update strategy that utilizes both the initial ground truth template and a supplementary updatable template, which considers the score of the predicted target for an efficient template update strategy by avoiding template updates during severe occlusion. All of the improvements were extensively evaluated and have achieved state-of-the-art performance in the VOT2018 and VOT2019 benchmarks. Our method (VPU-SiamM) has been submitted to the VOT-ST 2020 challenge, and it is ranked 16th out of 38 submitted tracking methods according to the Expected average overlap (EAO) metrics. VPU_SiamM Implementation can be found from the VOT2020 Trackers repository1.
Highlights
Visual object tracking (VOT), commonly referred to as target tracking, is an open problem in computer vision; this is due to a broad range of possible
One of the state-of-the-art tracking algorithms based on siamese networks called SiamMask has been used as a backbone, and two improvements have been affixed, each addressing different aspects of the tracking task
It is clear to conclude that the data augmentation technique has played an essential role in improving the overall performance of the tracking algorithm
Summary
Visual object tracking (VOT), commonly referred to as target tracking, is an open problem in computer vision; this is due to a broad range of possible1 https: //www.votchallenge.net/vot2020/trackers. htmlVisual Object Tracking applications and potential tracking challenges. Real-time visual target tracking plays an inevitable role in time-sensitive applications such as autonomous mobile robot control to keep track of the target of interest while the viewpoint is changing due to the movement of the target or the robot. In such a scenario, the tracking algorithm must be accurate and fast enough to detect sudden changes in the observed environment and act to prevent losing track of the quickly moving target of interest
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.