Pedestrian detection in infrared images using HOG, LBP, gradient magnitude and intensity feature channels

Recent work in monocular pedestrian detection is trying to improve the execution time while keeping the accuracy as high as possible. A popular and successful approach for monocular intensity pedestrian detection is based on the approximation (instead of computation) of image features for multiple scales based on the features computed on set of predefined scales. We port this idea to the infrared domain. Our contributions reside in the combination of four channel features, namely infrared, histogram of gradient orientations, normalized gradient magnitude and local binary patterns with the objective of detecting pedestrians for night vision applications dealing with far infrared sensors. Multiple scale feature computation is done by feature approximation. Another contribution is the study of different formulations for Local Binary Patterns like uniform patterns and rotation invariant patterns and their effect on detection performance. The detection speed is also boosted by the aid of a fast morphological based region of interest generator. We vary the number of approximated scales per octave and study the impact on execution time and accuracy. A reasonable result hits a speed of 18fps with a log average miss rate of 39%.