Aberrance suppressed spatio-temporal correlation filters for visual object tracking. (July 2021)

• Record Type: Journal Article
• Authors: Elayaperumal, Dinesh; Joo, Young Hoon
• Is Part Of: Pattern recognition. Volume 115(2021)
• Publication Date: 2021-07
• Subjects: Visual object tracking -- Correlation filter -- Spatio-temporal information -- Radical changes; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.107922
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Proposed a tracking algorithm concerning the spatial information and aberrance repressed-based correlation filter. The proposed method utilizes the spatial and temporal information of the target to avoid the boundary effects and drastic appearance changes. Introduces the aberrance term into proposed tracker to avoid tracking failure caused by sudden changes appeared in between the two frames. Provides an optimal solution for the model updating based on the ADMM method. The experimental evaluations and comparisons with existing trackers are provided by following benchmark datasets: OTB2013, OTB2015, TempleColor128 and UAV123. Abstract: The objective of the present study is to design a correlation filter-based tracking method for robust visual object tracking. In the literature, numerous tracking methods have been proposed based on discriminative correlation filter (DCF) and obtained impressive performance. However, existing algorithms still face difficulties such as partial occlusion, clutter background, uncertainties, boundary effects (especially when the target search area is small) and other challenging visual factors. Furthermore, during the target detection process, the sudden changes in objects caused by illumination variations and partial/full occlusion degrade the performance. To tackle the drawbacks mentioned earlier, we propose a tracking algorithm concerning the aberrance suppressed correlation filters with spatio-temporal information for visual tracking. Specifically, we introduce a spatial regularization term into the correlation filter to suppresses the boundary effects. Following that, a temporal regularization is adopted into the DCF-based framework to achieve a more robust appearance model and further enhance the tracking performance. In addition, we introduce an approach to suppress the aberrance in response maps caused by the sudden changes. Technically, our proposed method can be directly solved by using the alternating direction method of multipliers (ADMM) technique with a low computational cost. Finally, extensive experimental results on OTB2013, OTB2015, TempleColor128 and UAV123 datasets demonstrate that the proposed method performs favorably against state-of-the-art methods.