- freely available
Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions
AbstractThe lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.
Share & Cite This Article
Musleh, B.; García, F.; Otamendi, J.; Armingol, J.M.; De la Escalera, A. Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions. Sensors 2010, 10, 8028-8053.View more citation formats
Musleh B, García F, Otamendi J, Armingol JM, De la Escalera A. Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions. Sensors. 2010; 10(9):8028-8053.Chicago/Turabian Style
Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Mª; De la Escalera, Arturo. 2010. "Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions." Sensors 10, no. 9: 8028-8053.
Notes: Multiple requests from the same IP address are counted as one view.