Abstract
Object tracking is an important research direction in computer vision and is widely used in video surveillance, security monitoring, video analysis and other fields. Conventional tracking algorithms perform poorly in specific scenes, such as a target with fast motion and occlusion. The candidate samples may lose the true target due to its fast motion. Moreover, the appearance of the target may change with movement. In this paper, we propose an object tracking algorithm based on motion consistency. In the state transition model, candidate samples are obtained by the target state, which is predicted according to the temporal correlation. In the appearance model, we define the position factor to represent the different importance of candidate samples in different positions using the double Gaussian probability model. The candidate sample with highest likelihood is selected as the tracking result by combining the holistic and local responses with the position factor. Moreover, an adaptive template updating scheme is proposed to adapt to the target’s appearance changes, especially those caused by fast motion. The experimental results on a 2013 benchmark dataset demonstrate that the proposed algorithm performs better in scenes with fast motion and partial or full occlusion compared to the state-of-the-art algorithms.
Highlights
Object tracking is an important application for video sensor signal and information processing, which is widely applied in video surveillance, security monitoring, video analysis, and other areas. numerous methods have been proposed, it is still a challenging problem to implement object tracking in particular scenes, such as sports scenes for player tracking and in security scenes for criminal tracking
We propose an object-tracking algorithm based on motion consistency (MCT)
We propose a tracking algorithm based on motion consistency, in which the candidate samples are predicted by a state transition model based on target state prediction
Summary
Numerous methods have been proposed, it is still a challenging problem to implement object tracking in particular scenes, such as sports scenes for player tracking and in security scenes for criminal tracking. These scenes are characterized by fast motion of the target, occlusion and illumination variation. The particle filter is widely used in object tracking, which uses the Monte Carlo method to simulate the probability distribution and is effective in estimating the non-Gaussian and nonlinear states [1]. The state transition model and the appearance model are two important types of probabilistic models. The state transition model is used to predict the current target state based on the previous target states, which can be divided into Gaussian distribution model and constant velocity model
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
