Fast pedestrian detection based on a partial least squares cascade

In applications such as surveillance, pedestrian detection can be seen as a filtering stage which will locate the objects of interest so that higher level tasks, such as recognition, re-identification, action and activity recognition, can be performed considering only those objects. Therefore, it is imperative that the pedestrian detection task presents low computational cost. Several methods have been proposed to detect pedestrians in images and videos. However, a remaining challenge is to detect pedestrians with high accuracy at a very low computational cost. Towards accomplishing the goal of reducing the costs for pedestrian detection, we propose a cascade of rejection based on Partial Least Squares (PLS) and the variable selection method Variable Importance in Projection (VIP) combined with the propagation of latent variables through the stages. Our results show that the method reduces the computational cost by increasing the number of rejected background samples in earlier stages of the cascade.