Visual tracking with screening region enrichment and target validation

Abstract

The introduction of the one-stream one-stage framework has led to remarkable advances in visual object tracking, resulting in exceptional tracking performance. Most existing one-stream one-stage tracking pipelines have achieved a relative balance between accuracy and speed. However, they focus solely on integrating feature learning and relational modelling. In complex scenes, the tracking performance often falls short due to confounding factors such as changes in target scale, occlusion, and fast motion. In these cases, numerous trackers cannot sufficiently exploit the target feature information and face the dilemma of information loss. To address these challenges, we propose a screening enrichment for transformer-based tracking. Our method incorporates a screening enrichment module as an additional processing operation in the integration of feature learning and relational modelling. The module effectively distinguishes target areas within the search regions. It also enriches the associations between tokens of target area information. In addition, we introduce our box validation module. This module uses the target position information from the previous frame to validate and revise the target position in the current frame. This process enables more accurate target localization. Through these innovations, we have developed a powerful and efficient tracker. It achieves state-of-the-art performance on six benchmark datasets, including GOT-10K, LaSOT, TrackingNet, UAV123, TNL2K and VOT2020. On the GOT-10K benchmarks, Specifically, on the GOT-10K benchmarks, our proposed tracker reaches an impressive Success Rate (\(S{{R}_{0.5}}\)) of 85.4 and an Average Overlap (AO) of 75.3. Experimental results show that our proposed tracker outperforms other state-of-the-art trackers in terms of tracking accuracy.