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We propose a novel method for recognizing people in aerial surveillance videos. Aerial surveillance images cover a wide area at low resolution. In order to detect objects (e.g., pedestrians) from such videos, conventional methods either utilize appearance information from raw videos or extract blob information from background subtraction results. However, people seen in low resolution images have less appearance information, and hence are very difficulty to classify based on their appearance or blob size. In addition, due to heavy camera movements caused by aerial vehicle ego-motion and wind, the system is expected to generate many noisy false detections including parallax. The idea presented in this paper is to detect and classify objects from aerial videos based on their motion: we analyze a trajectory of each object candidate, deciding whether it is a person-of-interest or simple noise based on how it moved. After objects are tracked by a Kalman filter-based tracking, we represent their motion as multi-scale histograms of ‘orientation changes’, which efficiently captures movements displayed by objects. Random forest classifiers are applied to our new representation to make the decision. The experimental results illustrate that our approach recognizes objects-of-interest (i.e., humans) even when there exist a large number of false detection/tracking, and it does it more reliably compared to the approaches with previous paradigm.

Recognizing Humans in Motion: Trajectory-based Aerial Video Analysis