Pedestrian detection from still images based on multi-feature covariances
Yaping Liu, Jian Yao, Renping Xie, Sa Zhu · 2013
This paper targets the detection of pedestrians from still images, which focuses on developing robust feature representations that encode image regions as covariance matrices to support high accuracy pedestrian/non-pedestrian decisions. Firstly we utilize a fast method for computation of covariances based on integral images. By integrating the advantages of both covariance-based object detection and HOG-and FDF-based pedestrian detection, we then introduce four new feature representations for training a pedestrian detector: Covariance-based first-order Histogram of Oriented Gradient (Cov-HOG1), Covariance-based second-order Histogram of Oriented Gradient (Cov-HOG2), Covariance-based first-order Four Directional Features (Cov-FDF1), and Covariance-based second-order Four Directional Features (Cov-FDF2). To test our feature sets, we adopt a relatively simple learning framework that uses LogitBoost algorithm to classify each possible image region as a pedestrian or as a non-pedestrian. The experimental results show that the proposed algorithm obtains satisfactory pedestrian detection performances on the INRIA person datasets as well as images collected from Google and Flickr websites.