Introducing a Inter-frame Relational Feature Model for Pedestrian Detection

Pedestrian detection has been used with the help of various local features in still images such as histograms of oriented gradients (HOG), local binary patterns (LBP) and more recently, the histograms of optical flow (HOF). In order to improve the robustness of pedestrian detection, movement of people can be taken into the training process which has been done in the HOF descriptor. Optical flow is used to model the movement of a person and to detect actions in image sequences. For action recognition it is necessary to incorporate movement into models when using feature descriptors such as the HOF descriptor. In this paper we introduce a novel method to train and to detect human movement for pedestrian detection using relational gradient features within multiple consecutive frames. The goal of this descriptor is to detect pedestrians using multiple frames for moving cameras instead of static cameras. The relational features between consecutive frames help to robustly find pedestrians in image sequences due to a flexible detection algorithm. We demonstrate the robustness of the resulting feature model computed for a temporal time window of three frames. In our experiments we show the improvement regarding true positives as well as false positives using our inter-frame HOG (ifHOG) model compared to other feature descriptors.