Tiny Object Tracking with Proposal Position Enhancement

Tiny object tracking is challenging due to the target's weak appearance and features. The current state-of-theart approach for this task uses probabilistic regression based on discriminative correlation filters (DCF) to predict regression scores. However, the probabilistic regression equally relies on the position and size of the proposal box. In this letter, we point out that the position is of greater importance relative to the size when regressing the tiny object. To this end, we introduce the receptive field distance to define the quality of the proposal box, which places more emphasis on the position. By incorporating quality as an additional important factor for Monte Carlo sampling, the number of high quality proposals can be effectively increased, leading to regression optimization. Moreover, we propose a network to generate quality scores for proposals. The combination of probability and quality scores serves as a selection criterion for the optimal proposal, and can boost the tiny object tracking performance. Extensive experiments demonstrate the effectiveness of the proposed method. The code and model will be available at https://github.com/jankin987/track-mcsq