Enabling deformation slack in tracking with temporally even correlation filters

Discriminative correlation filters with temporal regularization have recently attracted much attention in mobile video tracking, due to the challenges of target occlusion and background interference. However, rigidly penalizing the variability of templates between adjacent frames makes trackers lazy for target evolution, leading to inaccurate responses or even tracking failure when deformation occurs. In this paper, we address the problem of instant template learning when the target undergoes drastic variations in appearance and aspect ratio. We first propose a temporally even model featuring deformation slack, which theoretically supports the ability of the template to respond quickly to variations while suppressing disturbances. Then, an optimal derivation of our model is formulated, and the closed form solutions are deduced to facilitate the algorithm implementation. Further, we introduce a cyclic shift methodology for mirror factors to achieve scale estimation of varying aspect ratios, thereby dramatically improving the cross-area accuracy. Comprehensive comparisons on seven datasets demonstrate our excellent performance: DroneTB-70, VisDrone-SOT2019, VOT-2019, LaSOT, TC-128, UAV-20L, and UAVDT. Our approach runs at 16.9 frames per second on a low-cost Central Processing Unit, which makes it suitable for tracking on drones. The code and raw results will be made publicly available at: https://github.com/visualperceptlab/TEDS.