Abstract
In computer vision applications, visual object tracking is a complex task in which object appearances change in the presence of illumination variation, occlusion, in-plane rotation, and fast motion. In the state-of-the-art approaches, trackers deal with the common model to address appearance variations with coexisting challenges. However, this approach is ineffective when dealing with simultaneous challenges because the object’s features differ due to the appearance variations. To alleviate these limitations, in this paper, a visual object tracking framework that relies on an object appearance feature update is proposed. The appearance tracking model was developed using templated spatial information and object features. The tracked object template is identified by comparing the tracked template from the previous frame with the directional templates in the current frame. To adapt to appearance variations, the proposed tracking model updates the tracked template feature vector, the motion parameters, and the spatial information as it tracks the target object in successive frames. The experimental results on challenging video sequences in object tracking benchmarks demonstrate that the proposed tracking model can track objects with a precision of 84.5% at a 10.1 fps tracking speed. The qualitative analysis shows that the proposed tracking model outperforms the related conventional trackers.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by PRV and AFA. The first draft of the manuscript was written by PRV, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Vadamala, P.R., Aklak, A.F. Discriminative appearance model with template spatial adjustment for visual object tracking. Soft Comput 27, 9787–9800 (2023). https://doi.org/10.1007/s00500-023-07820-x
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DOI: https://doi.org/10.1007/s00500-023-07820-x