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Keywords = fractional-gain Kalman filter

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21 pages, 5726 KiB  
Article
Efficient Online Object Tracking Scheme for Challenging Scenarios
by Khizer Mehmood, Ahmad Ali, Abdul Jalil, Baber Khan, Khalid Mehmood Cheema, Maria Murad and Ahmad H. Milyani
Sensors 2021, 21(24), 8481; https://doi.org/10.3390/s21248481 - 20 Dec 2021
Cited by 10 | Viewed by 3813
Abstract
Visual object tracking (VOT) is a vital part of various domains of computer vision applications such as surveillance, unmanned aerial vehicles (UAV), and medical diagnostics. In recent years, substantial improvement has been made to solve various challenges of VOT techniques such as change [...] Read more.
Visual object tracking (VOT) is a vital part of various domains of computer vision applications such as surveillance, unmanned aerial vehicles (UAV), and medical diagnostics. In recent years, substantial improvement has been made to solve various challenges of VOT techniques such as change of scale, occlusions, motion blur, and illumination variations. This paper proposes a tracking algorithm in a spatiotemporal context (STC) framework. To overcome the limitations of STC based on scale variation, a max-pooling-based scale scheme is incorporated by maximizing over posterior probability. To avert target model from drift, an efficient mechanism is proposed for occlusion handling. Occlusion is detected from average peak to correlation energy (APCE)-based mechanism of response map between consecutive frames. On successful occlusion detection, a fractional-gain Kalman filter is incorporated for handling the occlusion. An additional extension to the model includes APCE criteria to adapt the target model in motion blur and other factors. Extensive evaluation indicates that the proposed algorithm achieves significant results against various tracking methods. Full article
(This article belongs to the Special Issue Sensors for Object Detection, Classification and Tracking)
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