New Features and Insights for Pedestrian Detection

Despite impressive progress in people detection the performance on challenging datasets like Caltech Pedestrians or TUD-Brussels is still unsatisfactory. In this work we show that motion features derived from optic flow yield substantial improvements on image sequences, if implemented correctly—even in the case of low-quality video and consequently degraded flow fields. Furthermore, we introduce a new feature, self-similarity on color channels, which consistently improves detection performance both for static images and for video sequences, across different datasets. In combination with HOG, these two features outperform the state-of-the-art by up to 20%. Finally, we report two insights concerning detector evaluations, which apply to classifier-based object detection in general. First, we show that a commonly under-estimated detail of training, the number of bootstrapping rounds, has a drastic influence on the relative (and absolute) performance of different feature/classifier...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Stefan Walk, Nikodem Majer, Konrad Schindler, Bernt Schiele
• Keywords: Computer Vision, CVPR 2010, Detection, Detector Evaluation, Yield Substantial Improvements