Pedestrian detection from thermal images with a scattered difference of directional gradients feature descriptor

Pedestrian detection is a rapidly evolving research area in computer vision with great impact on the quality of people's daily life. In pedestrian detection, a robust feature descriptor that discriminates pedestrians from the background is a paramount step. Generally, pedestrians are detected with features extracted from visible images. However, those features can easily be contaminated by the changes of clothing color, illumination, body deformation, and complex backgrounds. These factors present great challenges for designing robust feature descriptors. In this study, we address this issue by proposing a new feature descriptor, namely, scattered difference of directional gradients (SDDG), for thermal images. Unlike visible images, thermal images are insensitive to illumination changes and immune to the variation of clothing color as well as the complexity of backgrounds. Compared with other feature descriptors, the SDDG captures more detailed local gradient information so that objects can be well described along certain directions. Experimental results demonstrate the comparable performance of the proposed feature descriptor with well-known feature descriptors, e.g. histogram of oriented gradients (HOG) and Haar wavelets (HWs).