Traditional object trackers struggle in degraded scenarios, lacking sufficient appearance details of moving targets for precise tracking. Recent trackers have integrated highfrequency event signals to assist tracking. However, they neglect the high-temporalresolution motion information inherent in events, limiting their performance especially in occlusion and background clutter. To address these challenges, we propose HFTrack, a novel tracker designed to fully leverage the spatio-temporal high-frequency information within event signals, thereby enhancing the tracking performance. Specifically, we introduce a frequency-based feature enhancement module, which enriches the frame feature with high-frequency components from events in frequency space, capturing detailed appearance information of moving targets. Additionally, we propose a spatio-temporal information decoder with an auto-regressive temporal query, integrating both historical motion cues from events and enhanced spatial features for robust target localization. Experimental results demonstrate that our HFTrack significantly outperforms existing trackers, showcasing its strong ability to track the target under challenging conditions.
Traditional object trackers struggle in degraded scenarios, lacking sufficient appearance details of moving targets for precise tracking. Recent trackers have integrated highfrequency event signals to assist tracking. However, they neglect the high-temporalresolution motion information inherent in events, limiting their performance especially in occlusion and background clutter. To address these challenges, we propose HFTrack, a novel tracker designed to fully leverage the spatio-temporal high-frequency information within event signals, thereby enhancing the tracking performance. Specifically, we introduce a frequency-based feature enhancement module, which enriches the frame feature with high-frequency components from events in frequency space, capturing detailed appearance information of moving targets. Additionally, we propose a spatio-temporal information decoder with an auto-regressive temporal query, integrating both historical motion cues from events and enhanced spatial features for robust target localization. Experimental results demonstrate that our HFTrack significantly outperforms existing trackers, showcasing its strong ability to track the target under challenging conditions.