A multi-scale far infrared night traffic pedestrian recognition scheme

Jingfu LI; Zhiqiang YANG

doi:10.3969/j.issn.1007-5461. 2016.04.005

[1] Traffic Management Bureau of Ministry of Public Security. 2009 national road traffic accident[OL]. http://www.mps.gov.cn/n16/n1252/n1837/n2557/2276407.html, 2010.

[3] Borhade S, Shah M, Jadhav P, et al. Advanced driver assistance system[C]. Proceedings of International Conference on Sensing Technology (ICST). Kolkata, India, 2012: 718-722.

[4] Olmeda D, de la Escalera A, Armingol J M. Contrast invariant features for human detection in far infrared images[C]. Proceedings of IEEE Intelligent Vehicles Symposium (IV). Alcala de Henares, Spain, 2012: 117-122.

[6] Cutler R, Davis L S. Robust real-time periodic motion detection, analysis and applications[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 781-796.

[7] Olmeda D, de la Escalera A, Armingol J M. Detection and tracking of pedestrians in infrared images[C]. Proceeding of International Conference on Signals, Circuits and Systems (SCS). Medenine, Tunisia: IEEE, 2009: 1-6.

[8] Nanda H, Davis L. Probabilistic template based pedestrian detection in infrared videos[C]. Proceedings of IEEE Intelligent Vehicle Symposium (IV), Versailles, France: IEEE, 2002: 15-20.

[11] Ge J F, Luo Y P, Gyomei T. Real-time pedestrian detection and tracking at nighttime for driver-assistance systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(2): 283-298.

[13] Gavrila D M. A Bayesian, exemplar-based approach to hierarchical shape matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(8): 1408-1421.

[14] Gavrila D M, Munder S. Multi-cue pedestrian detection and tracking from a moving vehicle[J]. International Journal of Computer Vision, 2007, 73(1): 41-59.

[15] Giebel J,Gavrila D M, Schnrr C. A Bayesian framework for multi-cue 3D object tracking[J]. Lecture Notes in Computer Science, 2004, 3024: 241-252.

[17] Bertozzi M, Broggi A, Gomez C H, et al. Pedestrian detection in far infrared images based on the use of probabilistic templates[C]. Proceeding of IEEE Intelligent Vehicles Symposium (IV). Istanbul, Turkey, 2007: 327-332.