DenseTrack：通过密度感知运动-外观协同实现基于无人机的群体跟踪
DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy
AI总结：
针对无人机视角下小尺寸密集人群定位与跟踪困难的问题，提出DenseTrack框架，融合群体密度估计、运动偏移与视觉语言模型外观线索，并利用匈牙利算法实现跨帧匹配，在DroneCrowd数据集上表现优越。
摘要：
基于无人机的群体跟踪在从空中视角准确识别和监控物体方面面临困难，这主要是由于物体尺寸小且彼此距离很近，从而使定位和跟踪都变得复杂。为了应对这些挑战，我们提出了Density-aware Tracking（DenseTrack，密度感知跟踪）框架。DenseTrack利用群体计数来精确确定物体位置，融合视觉和运动线索来改善小尺度物体的跟踪。它专门解决跨帧运动问题，以提高跟踪的准确性和可靠性。DenseTrack采用群体密度估计作为锚点，用于视频帧内精确的物体定位。这些估计与来自跟踪网络的运动和位置信息相合并，其中运动偏移作为关键的跟踪线索。此外，DenseTrack利用视觉语言模型的见解增强了区分小尺度物体的能力，将外观与运动线索相结合。该框架利用匈牙利算法确保跨帧个体的准确匹配。在DroneCrowd数据集上的演示表明，我们的方法展现出优越的性能，证实了其在无人机捕获场景中的有效性。
英文摘要：
Drone-based crowd tracking faces difficulties in accurately identifying and monitoring objects from an aerial perspective, largely due to their small size and close proximity to each other, which complicates both localization and tracking. To address these challenges, we present the Density-aware Tracking (DenseTrack) framework. DenseTrack capitalizes on crowd counting to precisely determine object locations, blending visual and motion cues to improve the tracking of small-scale objects. It specifically addresses the problem of cross-frame motion to enhance tracking accuracy and dependability. DenseTrack employs crowd density estimates as anchors for exact object localization within video frames. These estimates are merged with motion and position information from the tracking network, with motion offsets serving as key tracking cues. Moreover, DenseTrack enhances the ability to distinguish small-scale objects using insights from the visual-language model, integrating appearance with motion cues. The framework utilizes the Hungarian algorithm to ensure the accurate matching of individuals across frames. Demonstrated on DroneCrowd dataset, our approach exhibits superior performance, confirming its effectiveness in scenarios captured by drones.
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