Pedestrian Detection in Low-Resolution Imagery by Learning Multi-scale Intrinsic Motion Structures (MIMS)

Detecting pedestrians at a distance from large-format wide-area imagery is a challenging problem because of low ground sampling distance (GSD) and low frame rate of the imagery. In such a scenario, the approaches based on appearance cues alone mostly fail because pedestrians are only a few pixels in size. Frame-differencing and optical flow based approaches also give poor detection results due to noise, camera jitter and parallax in aerial videos. To overcome these challenges, we propose a novel approach to extract Multi-scale Intrinsic Motion Structure features from pedestrian's motion patterns for pedestrian detection. The MIMS feature encodes the intrinsic motion properties of an object, which are location, velocity and trajectory-shape invariant. The extracted MIMS representation is robust to noisy flow estimates. In this paper, we give a comparative evaluation of the proposed method and demonstrate that MIMS outperforms the state of the art approaches in identifying pedestrians from low resolution airborne videos.