Abstract
Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.
Highlights
Visual object tracking (VOT) has emerged as a dynamic study area due to its utilization in a wide range of applications such as human action recognition [1,2,3], traffic monitoring [4,5], pellet ore phase [6], smart city [7], embedded system [8], surveillance [9,10,11] and medical diagnosis [12,13]
We introduce an effective method in which the object can be tracked accurately by utilizing extended Kalman filter (EKF) detection for nonlinear target motion
The proposed tracker was compared quantitatively with existing tracking methods based on distance precision rate (DPR) and center location error (CLE)
Summary
Visual object tracking (VOT) has emerged as a dynamic study area due to its utilization in a wide range of applications such as human action recognition [1,2,3], traffic monitoring [4,5], pellet ore phase [6], smart city [7], embedded system [8], surveillance [9,10,11] and medical diagnosis [12,13]. Target tracking methods, being classified as generative [17] and discriminative [18], are widely referred in literature with prominent applications. Generative tracking methods learn the appearance model of the target and search for the highest matching score. These methods achieve good tracking results at the expense of computational cost. Discriminative tracking methods treat it as binary classification and achieve favorable results. Tracking in these methods might get affected when training data is small
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
