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To promote the developments of object detection, tracking and countingalgorithms in drone-captured videos, we construct a benchmark with a newdrone-captured largescale dataset, named as DroneCrowd, formed by 112 videoclips with 33,600 HD frames in various scenarios. Notably, we annotate 20,800people trajectories with 4.8 million heads and several video-level attributes.Meanwhile, we design the Space-Time Neighbor-Aware Network (STNNet) as a strongbaseline to solve object detection, tracking and counting jointly in densecrowds. STNNet is formed by the feature extraction module, followed by thedensity map estimation heads, and localization and association subnets. Toexploit the context information of neighboring objects, we design theneighboring context loss to guide the association subnet training, whichenforces consistent relative position of nearby objects in temporal domain.Extensive experiments on our DroneCrowd dataset demonstrate that STNNetperforms favorably against the state-of-the-arts.

Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark