Pedestrian Detection Optimization Algorithm Based on Low-Altitude UAV

Abstract

Pedestrian detection is one of the critical benchmarks for object detection in computer vision. In recent years, more effective detectors and features, such as Histograms of Oriented Gradients (HOG) have been proposed. The process of HOG features calculation is slow, and the features cannot satisfy represent the human body. Therefore, we adopt the multi-channel features, and propose a new improved method for accelerated integral image, the execution time of which is less than the original method. In addition, we apply novel multi-scales detection to detect new scenario, which is based on the low-altitude UAV. Under such scenario our algorithm can handle the changing in pedestrian posture and occlusion cues. The experimental results indicate that our algorithm is rapid and efficient under dynamic camera, comparing with other methods in INRIA dataset.