Adaptive Framework for Robust Visual Tracking

Abdelpakey, Mohamed H. and Shehata, Mohamed S. and Mohamed, Mostafa M. and Gong, Minglun (2018) Adaptive Framework for Robust Visual Tracking. IEEE Access, 6. pp. 55273-55283. ISSN 2169-3536

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Visual tracking is a difficult and challenging problem, for numerous reasons such as small object size, pose angle variations, occlusion, and camera motion. Object tracking has many real-world applications such as surveillance systems, moving organs in medical imaging, and robotics. Traditional tracking methods lack a recovery mechanism that can be used in situations when the tracked objects drift away from ground truth. In this paper, we propose a novel framework for tracking moving objects based on a composite framework and a reporter mechanism. The composite framework tracks moving objects using different trackers and produces pairs of forward/backward tracklets. A robustness score is then calculated for each tracker using its forward/backward tracklet pair to find the most reliable moving object trajectory. The reporter serves as the recovery mechanism to correct the moving object trajectory when the robustness score is very low, mainly using a combination of particle filter and template matching. The proposed framework can handle partial and heavy occlusions; moreover, the structure of the framework enables integration of other user-specific trackers. Extensive experiments on recent benchmarks show that the proposed framework outperforms other current state-of-the-art trackers due to its powerful trajectory analysis and recovery mechanism; the framework improved the area under the curve from 68% to 70.8% on OTB-100 benchmark.

Item Type: Article
Item ID: 13733
Additional Information: Memorial University Open Access Author's Fund
Keywords: Composite framework, trajectory analysis, virtual vectors, reporter, adaptive framework, visual tracking, unmanned aerial vehicle
Department(s): Engineering and Applied Science, Faculty of
Date: 24 September 2018
Date Type: Publication
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