Comparison between infrared-image-based and visible-image-based approaches for pedestrian detection

In order to improve the safety of night driving, automatic pedestrian detection has received more and more attraction. Since reliability is the most important issue in these systems, multi-dimensional-feature-based segmentation and classification needs to be introduced, and each axis should be efficient and be as much independent (to each other) as possible. To choose effective multi-dimensional features for infrared-image-based detection, the paper first investigates the possibilities of reusing available features for visible images by analyzing the different properties of infrared images and visible images. To take advantage of unique properties of infrared images, we propose the following novel features: special projection feature for segmentation, and two-axis pixel-distribution feature for classification. The segmentation based on new features does not depend on many assumptions and is shape-independent, thus avoiding brute-force multiple templates and multi-scale pyramid searching. The novel classification features include histogram feature and inertial feature that are independent and complimentary, thus the two-dimensional fusion-based classification significantly improves detection accuracy. These proposed features are independent from conventional pixel-array feature, and can be further fused with other general pedestrian detection features to improve simplicity, speed, and reliability.