Feature selection for automotive object detection tasks - A study

For object detection in monocular images, the Boosted Cascade [1] has become the standard approach for driver assistance systems. This paper studies the discriminative power of different features for common automotive object detection tasks: pedestrian and vehicle detection using infrared cameras at night, as well as pedestrian and vehicle detection in daylight conditions. It is shown that the use of intra-stage information propagation with Activation History Features (AHFs) [2] significantly speeds up the detection at the same detection rate. Thus, AHFs offer a speedup at no cost.