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We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker. The baseline pre-trained short-term tracker is SuperDiMP which combines the bounding-box regressor of PrDiMP with the standard DiMP classifier. Our tracker RLT-DiMP improves SuperDiMP in the follow- ing three aspects: (1) Uncertainty reduction using random erasing: To make our model robust, we exploit an agreement from multiple im- ages after erasing random small rectangular areas as a certainty. And then, we correct the tracking state of our model accordingly. (2) Ran- dom search with spatio-temporal constraints: we propose a robust ran- dom search method with a score penalty applied to prevent the prob- lem of sudden detection at a distance. (3) Background augmentation for more discriminative feature learning: We augment various backgrounds that are not included in the search area to train a more robust model in the background clutter. In experiments on the VOT-LT2020 bench- mark dataset, the proposed method achieves comparable performance to the state-of-the-art long-term trackers. The source code is available at: https://github.com/bismex/RLT-DIMP. 

Developing computational methods for bodily expressed emotion understanding can benet from knowledge and approaches of multiple fields, including computer vision, robotics, psychology/psychiatry, graphics, data mining, machine learning, and movement analysis. The panel, consisting of active researchers in some closely related fields, attempts to open a discussion on the future of this new and exciting research area. This paper documents the opinions expressed by the individual panelists.