TY - GEN
T1 - Increasing driving safety with a multiple vehicle detection and tracking system using ongoing vehicle shadow information
AU - Aytekin, Bureu
AU - Altuǧ, Erdinç
PY - 2010
Y1 - 2010
N2 - This paper proposes a vehicle detection and tracking system based on processing monochrome images captured by a single camera. The work has mainly been focused on detecting and tracking vehicles in daylight conditions, viewed from inside a vehicle. Unlike previous work, this approach uses vehicle shadow clues and vehicle edge information to obtain cost effective and fast estimation. The proposed method includes road area finding which has been implemented by a lane detection algorithm to avoid false detections of vehicles caused by the distraction of background objects. Assuming that lanes are successfully detected, vehicle presence inside the road area is hypothesized by using "shadow" as a cue. Hypothesized vehicle locations are verified using vertical edges. After extracting vehicles, the algorithm effectively tracks them using a Kalman filter based tracking algorithm. A vehicle has been instrumented with various sensors for the experiments. Several sequences from real traffic situations have been tested, obtaining highly accurate multiple vehicle detections. Tracking information is used to estimate time-to-collision (TTC) and waru the driver for a possible collision.
AB - This paper proposes a vehicle detection and tracking system based on processing monochrome images captured by a single camera. The work has mainly been focused on detecting and tracking vehicles in daylight conditions, viewed from inside a vehicle. Unlike previous work, this approach uses vehicle shadow clues and vehicle edge information to obtain cost effective and fast estimation. The proposed method includes road area finding which has been implemented by a lane detection algorithm to avoid false detections of vehicles caused by the distraction of background objects. Assuming that lanes are successfully detected, vehicle presence inside the road area is hypothesized by using "shadow" as a cue. Hypothesized vehicle locations are verified using vertical edges. After extracting vehicles, the algorithm effectively tracks them using a Kalman filter based tracking algorithm. A vehicle has been instrumented with various sensors for the experiments. Several sequences from real traffic situations have been tested, obtaining highly accurate multiple vehicle detections. Tracking information is used to estimate time-to-collision (TTC) and waru the driver for a possible collision.
KW - Collision warning
KW - Computer vision
KW - Intelligent vehicles
KW - Vehicle detection
KW - Vehicle tracking
UR - http://www.scopus.com/inward/record.url?scp=78751492145&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2010.5641879
DO - 10.1109/ICSMC.2010.5641879
M3 - Conference contribution
AN - SCOPUS:78751492145
SN - 9781424465880
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3650
EP - 3656
BT - 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
T2 - 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
Y2 - 10 October 2010 through 13 October 2010
ER -