Looking at Pedestrians at Different Scales: A Multiresolution Approach and Evaluations

Typically, in a detector framework, the model size is fixed at the size of the smallest object to be detected, and larger objects are detected by scaling the input image. The information lost due to scaling could be vital for accurately detecting large objects, which is an essential task for vision-based driver-assistance systems. To this end, we evaluate a multiresolution detector framework by training models at different sizes and demonstrate its effectiveness on a state-of-the-art pedestrian detector. Our comprehensive evaluation demonstrates meaningful improvement in detector performance. On the KITTI dataset under moderate difficulty settings, we achieve a 6% increase in the detector's average precision over the baseline single-resolution result on the KITTI benchmark. Further insights into the detector's improvements are provided using a fine-grained analysis of the detector's performance at various threshold settings.