Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Sensors

Abdul Jalil¹  Khizer Mehmood¹  Baber Khan¹  Maria Murad¹  AhmadH. Milyani²  Ahmad Ali³  KhalidMehmood Cheema⁴ 

[1] Department of Electrical Engineering, International Islamic University, Islamabad 44000, Pakistan;Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;Department of Software Engineering, Bahria University, Islamabad 44000, Pakistan;School of Electrical Engineering, Southeast University, Nanjing 210096, China;

关键词: spatio-temporal context;    object tracking;    scale correlation filter;    extended kalman filter;   

DOI  :  10.3390/s21082841

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown