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Visual data collected from Unmanned Aerial Vehicles (UAVs) has opened a newfrontier of computer vision that requires automated analysis of aerialimages/videos. However, the existing UAV datasets primarily focus on objectdetection. An object detector does not differentiate between the moving andnon-moving objects. Given a real-time UAV video stream, how can we bothlocalize and classify the moving objects, i.e. perform moving objectrecognition (MOR)? The MOR is one of the essential tasks to support various UAVvision-based applications including aerial surveillance, search and rescue,event recognition, urban and rural scene understanding.To the best of ourknowledge, no labeled dataset is available for MOR evaluation in UAV videos.Therefore, in this paper, we introduce MOR-UAV, a large-scale video dataset forMOR in aerial videos. We achieve this by labeling axis-aligned bounding boxesfor moving objects which requires less computational resources than producingpixel-level estimates. We annotate 89,783 moving object instances collectedfrom 30 UAV videos, consisting of 10,948 frames in various scenarios such asweather conditions, occlusion, changing flying altitude and multiple cameraviews. We assigned the labels for two categories of vehicles (car and heavyvehicle). Furthermore, we propose a deep unified framework MOR-UAVNet for MORin UAV videos. Since, this is a first attempt for MOR in UAV videos, we present16 baseline results based on the proposed framework over the MOR-UAV datasetthrough quantitative and qualitative experiments. We also analyze themotion-salient regions in the network through multiple layer visualizations.The MOR-UAVNet works online at inference as it requires only few past frames.Moreover, it doesn't require predefined target initialization from user.Experiments also demonstrate that the MOR-UAV dataset is quite challenging.

MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos