How to improve the HOG detector in the UAV context

Abstract

The well known HOG (Histogram of Oriented Gradients) of Dalal and Triggs is commonly used for pedestrian detection from 2d moving embedded cameras (driving assistance) or static cameras (video surveillance). In this paper we show how to use and improve the HOG detector in the UAV context. In order to increase the elevation angular robustness we propose to use a more appropriate training dataset and sliding windows. We show results on synthetic images.

Human detection in uncluttered environments: From ground to UAV view

“…Blondel et al showed that the robustness to the elevation angle can be improved on synthetic images by a multi-view training at different elevations [16]. When the elevation angle is greater than 45degrees significantly better results were obtained for detecting 3d models.…”

Evaluating Sector Ring Histogram of Oriented Gradients Filter in Locating Humans Within Uav Images

“…It is of great interest to users and scientists to develop systems for the rapid identification of injured persons following natural disasters in unmanned aerial vehicles (UAV) images (Jingxuan et al, 2016). Unfortunately, when UAV images are used for human sensing with other challenges, the use of a UAV as an image platform introduces certain other problems (Blondel, 2013). Because pictures are taken from above, individuals can be quite different from those on the ground.…”