1Monocular Pedestrian Detection: Survey and Experiments Markus Enzweiler1 Student Member, IEEE and

Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspective. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade [74], HOG/linSVM [11], NN/LRF [75] and combined shape-texture detection [23]. Experiments are performed on an extensive dataset captured on-board a vehicle driving through urban environment. The dataset includes many thousands of training samples as well as a 27 minute test sequence involving more than 20000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection on-board a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The dataset (8.5GB) is made public for benchmarking purposes