Using Multi-Stage Features in Fast R-CNN for Pedestrian Detection

Pedestrian detection and tracking remains popular issue in computer vision, with many applications in robotics, surveillance, security and telecare systems, especially when connected with Smart Cities and Smart Destinations. As a particular case of object detection, pedestrian detection in general is a difficult task due to a large variability of features due to different scales, views and occlusion. Typically, smaller and occluded pedestrians are hard to detect due to fewer discriminative features if compared to large-size, visible pedestrians. In order to overcome this, we use convolutional features from different stages in a deep Convolutional Neural Network (CNN), with the idea of combining more global features with finer details. In this paper we present an object detection framework based on multi-stage convolutional features for pedestrian detection. This framework extends the Fast R-CNN framework for the combination of several convolutional features from different stages of the used CNN to improve the network's detection accuracy. The Caltech Pedestrian dataset was used to train and evaluate the proposed method.