Taking a deeper look at pedestrians

Jan Hosang, Mohamed Omran, Rodrigo Benenson, Bernt Schiele
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
In this paper we study the use of convolutional neural networks (convnets) for the task of pedestrian detection. Despite their recent diverse successes, convnets historically underperform compared to other pedestrian detectors. We deliberately omit explicitly modelling the problem into the network (e.g. parts or occlusion modelling) and show that we can reach competitive performance without bells and whistles. In a wide range of experiments we analyse small and big convnets, their architectural
more » ... their architectural choices, parameters, and the influence of different training data, including pretraining on surrogate tasks. We present the best convnet detectors on the Caltech and KITTI dataset. On Caltech our convnets reach top performance both for the Caltech1x and Caltech10x training setup. Using additional data at training time our strongest convnet model is competitive even to detectors that use additional data (optical flow) at test time.
doi:10.1109/cvpr.2015.7299034 dblp:conf/cvpr/HosangOBS15 fatcat:cjcmsf7uwvcidor4ftr7gwxzsy