We present a probabilistic framework for the joint estimation of pedestrian head and body orientation from a mobile stereo vision platform. For both head and body parts, we convert the responses of a set of orientation-specific detectors into a (continuous) probability density function. The parts are localized by means of a pictorial structure approach, which balances part-based detector responses with spatial constraints. Head and body orientations are estimated jointly to account for anatomical constraints. The joint single-frame orientation estimates are integrated over time by particle filtering. The experiments involved data from a vehicle-mounted stereo vision camera in a realistic traffic setting; 65 pedestrian tracks were supplied by a state-of-the-art pedestrian tracker. We show that the proposed joint probabilistic orientation estimation framework reduces the mean absolute head and body orientation error up to 15° compared with simpler methods. This results in a mean absolute head/body orientation error of about 21°/19°, which remains fairly constant up to a distance of 25 m. Our system currently runs in near real time (8–9 Hz).
A Probabilistic Framework for Joint Pedestrian Head and Body Orientation Estimation
IEEE Transactions on Intelligent Transportation Systems ; 16 , 4 ; 1872-1882
2015-08-01
2110865 byte
Article (Journal)
Electronic Resource
English
JOINT PROBABILISTIC PEDESTRIAN HEAD AND BODY ORIENTATION ESTIMATION
British Library Conference Proceedings | 2014
|A Probabilistic Framework for Joint Pedestrian Head and Body Orientation Estimation
Online Contents | 2015
|A Novel Pedestrian Orientation Estimation Method
TIBKAT | 2020
|JOINT PROBABILISTIC DATA ASSOCIATION FUSION APPROACH FOR PEDESTRIAN DETECTION
British Library Conference Proceedings | 2013
|