An Optimized Sliding Window Approach to Pedestrian Detection
While a large number of surveillance cameras available nowadays provide a safe environment, the huge amount of data generated by them prevents a manual processing, requiring the application of automated methods to understand the scene. However, the majority of the currently available methods are still unable to process this amount of data in real time, mainly those focusing on pedestrian detection. To optimize pedestrian detection methods, this work proposes a novel approach that performs a random filtering supported by the Maximum Search Problem theorem to select a very small number from all possible detection windows. Although the random filtering is able to select regions that capture every person on an image, some windows can cover only parts of a person, diminishing the accuracy. To solve that, a regression is applied to adjust the windows to the person's location. The computational cost reduction comes from the fact that the proposed approach does not need to perform any processing while selecting windows, differently from cascades of rejection that must evaluate at least simple features for every window. The experiments performed using a pedestrian detection based on Partial Least Squares show that the approach is effective in both accuracy and computational cost reduction.