A unified decision-driven framework: Long-term tracking via visual-language models with motion estimation

Current vision-language multimodal long-term tracking methods are highly dependent on large-scale data training and complex cross-modal models. This not only leads to substantial computational overhead but also restricts their deployment and application in resource-constrained scenarios. To resolve this contradiction, we propose a training-free, fast-response post-processing tracking framework named VL-METrack, which dynamically fuses motion estimation and localization networks. This framework transforms the long-term tracking task from a data-driven model training problem into a heuristic decision-driven online adaptive decision-making problem. Specifically, we first adopt a general pre-trained network to provide initial target localization and confidence assessment. When the confidence indicates tracking ambiguity, instead of relying on deeper networks, our method leverages a collaborative processing mechanism consisting of optical flow and correlation filters. This mechanism dynamically fuses optical flow and the localization network through confidence based adaptive weighting, while incorporating an error correction mechanism that judges the rate of change in target orientation. Subsequently, we employ a dynamic template update strategy and establish a global re-detection mechanism, maintaining template timeliness through an adaptive update process and determining the optimal position via multi-template comparison within an expanded search region. Experimental results on the OTB, LaSOT, and TNL2K datasets demonstrate that our method achieves performance comparable to state-of-the-art approaches while maintaining low computational complexity and reduced resource consumption. The code is available at: https://github.com/yll1214/VL-METrack.