Detecting Pedestrians by Learning Shapelet Features

In this paper, we address the problem of detecting pedestrians in still images. We introduce an algorithm for learning shapelet features, a set of mid?level features. These features are focused on local regions of the image and are built from low?level gradient information that discriminates between pedestrian and non?pedestrian classes. Using AdaBoost, these shapelet features are created as a combination of oriented gradient responses. To train the final classifier, we use AdaBoost for a second time to select a subset of our learned shapelets. By first focusing locally on smaller feature sets, our algorithm attempts to harvest more useful information than by examining all the low?level features together. We present quantitative results demonstrating the effectiveness of our algorithm. In particular, we obtain an error rate 14 percentage points lower (at 10-6 FPPW) than the previous state of the art detector of Dalal and Triggs [1] on the INRIA dataset.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Payam Sabzmeydani, Greg Mori
• Keywords: Computer Vision, CVPR 2007, Inria Dataset, Low Level Gradient Information, Oriented Gradient Responses, Rate 14 Percentage, Shapelet Features