Pedestrian Recognition in Aerial Video Using Saliency and Multi-Features

 Abstract—Pedestrian recognition in aerial video is a challenge problem for the problem of low resolution, camera movement and target's blurred detail in aerial video. This paper proposes weighted region matching algorithm with Kalman filter, Multi-features fusion model and saliency segmentation (KMFS-WRM) to detect and recognize pedestrian. The KMFS-WRM algorithm first uses Kalman filter algorithm to mark candidate's region, which can avoid the problem of selecting candidates under supervision. Then we proposed the fusion algorithm of multi-feature, including HOG, LBP and SIFT features, namely HLS model to detect the pedestrian in aerial video. Our proposed detection method is robust for whether the camera is moving. And instructing human percept and concept, we segment the pedestrians in marked region using Context-Aware saliency detection algorithm that proposed by Goferman et al. and revised the segmentation results by HST model (Head Shoulder and Torso) and AAM model (Active Appearance Model) to obtain the candidates set. Last the matching of voter and candidates set using weighted region matching algorithm. Experimental results in complex aerial video demonstrated that our KMFS-WRM algorithm not only cuts down calculated complexity, but also improves adaptive and real-time ability. Moreover proposed method outperforms recent state-of-the-art methods.