TY - GEN
T1 - The recognition and tracking of traffic lights based on color segmentation and CAMSHIFT for intelligent vehicles
AU - Gong, Jianwei
AU - Jiang, Yanhua
AU - Xiong, Guangming
AU - Guan, Chaohua
AU - Tao, Gang
AU - Chen, Huiyan
PY - 2010
Y1 - 2010
N2 - The recognition and tracking of traffic lights for intelligent vehicles based on a vehicle-mounted camera are studied in this paper. The candidate region of the traffic light is extracted using the threshold segmentation method and the morphological operation. Then, the recognition algorithm of the traffic light based on machine learning is employed. To avoid false negatives and tracking loss, the target tracking algorithm CAMSHIFT (Continuously Adaptive Mean Shift), which uses the color histogram as the target model, is adopted. In addition to traffic signal pre-processing and the recognition method of learning, the initialization problem of the search window of CAMSHIFT algorithm is resolved. Moreover, the window setting method is used to shorten the processing time of the global HSV color space conversion. The real vehicle experiments validate the performance of the presented approach.
AB - The recognition and tracking of traffic lights for intelligent vehicles based on a vehicle-mounted camera are studied in this paper. The candidate region of the traffic light is extracted using the threshold segmentation method and the morphological operation. Then, the recognition algorithm of the traffic light based on machine learning is employed. To avoid false negatives and tracking loss, the target tracking algorithm CAMSHIFT (Continuously Adaptive Mean Shift), which uses the color histogram as the target model, is adopted. In addition to traffic signal pre-processing and the recognition method of learning, the initialization problem of the search window of CAMSHIFT algorithm is resolved. Moreover, the window setting method is used to shorten the processing time of the global HSV color space conversion. The real vehicle experiments validate the performance of the presented approach.
UR - https://www.scopus.com/pages/publications/77956529508
U2 - 10.1109/IVS.2010.5548083
DO - 10.1109/IVS.2010.5548083
M3 - Conference contribution
AN - SCOPUS:77956529508
SN - 9781424478668
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 431
EP - 435
BT - 2010 IEEE Intelligent Vehicles Symposium, IV 2010
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference Information Visualisation, IV 2010
Y2 - 21 June 2010 through 24 June 2010
ER -