Citation: | GUO Ai-ying, XU Mei-hua, RAN Feng, WANG Qi. Model of real-time pedestrian detection under vehicle environment based on CS-SD[J]. Journal of Traffic and Transportation Engineering, 2016, 16(6): 132-139. |
[1] |
BENENSON R, OMRAN M, HOSANG J, et al. Ten years of pedestrian detection, what have we learned?[J]. Lecture Notes in Computer Science, 2015, 8926: 613-627.
|
[2] |
DIXIT R S, GANDHE S T. Pedestrian detection system for ADAS using Friendly ARM[C]//IEEE. 2015International Conference on Energy Systems and Applications. New York: IEEE, 2015: 557-560.
|
[3] |
GERÓNIMO D, LÓPEZ A M, SAPPA A D, et al. Survey of pedestrian detection for advanced driver assistance systems[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(7): 1239-1258. doi: 10.1109/TPAMI.2009.122
|
[4] |
HAJEK W, GAPONOVA I, FLEISCHER K H, et al. Workloadadaptive cruise control—a new generation of advanced driver assistance systems[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2013, 20: 108-120. doi: 10.1016/j.trf.2013.06.001
|
[5] |
DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//IEEE. 2005IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2005: 886-893.
|
[6] |
WANG Xiao-yu, HAN T X, YAN Shui-cheng. An HOGLBP human detector with partial occlusion handling[C]//IEEE. 2009 IEEE International Conference on Computer Vision. New York: IEEE, 2009: 32-39.
|
[7] |
FELZENSZWALB P, GIRSHICK R, MCALLESTER D, et al. Visual object detection with deformable part models[J]. Communications of the ACM, 2013, 56(9): 97-105. doi: 10.1145/2494532
|
[8] |
FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained partbased models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645. doi: 10.1109/TPAMI.2009.167
|
[9] |
OUYANG Wan-li, ZENG Xing-xu, WANG Xiao-gang. Singlepedestrian detection aided by two-pedestrian detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1875-1889. doi: 10.1109/TPAMI.2014.2377734
|
[10] |
DOLLÁR P, WOJEK C, SCHIELE B, et al. Pedestrian detection: an evaluation of the state of the art[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(4): 743-761. doi: 10.1109/TPAMI.2011.155
|
[11] |
FELZENSZWALB P, MCALLESTER D, RAMANAN D. A discriminatively trained, multiscale, deformable part model[C]//IEEE. 2008 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2008: 1-8.
|
[12] |
ZHANG Xiao-wei, HU Hai-miao, JIANG Fan, et al. Pedestrian detection based on hierarchical co-occurrence model for occlusion handling[J]. Neurocomputing, 2015, 168: 861-870. doi: 10.1016/j.neucom.2015.05.038
|
[13] |
NEHANIV C L, DAUTENHAHN K, KUBACKI J, et al. A methodological approach relating the classification of gesture to identification of human intent in the context of humanrobot interaction[C]//IEEE. 2005IEEE International Workshop on Robots and Human Interactive Communication. New York: IEEE, 2005: 371-377.
|
[14] |
CHO H, RYBSKI P E, BAR-HILLEL A, et al. Real-time pedestrian detection with deformable part models[C]//IEEE. 2012Intelligent Vehicles Symposium. New York: IEEE, 2012: 1035-1042.
|
[15] |
CHEN Xiao-feng, HENRICKSON K, WANG Yin-hai. Kinectbased pedestrian detection for crowded scenes[J]. ComputerAided Civil and Infrastructure Engineering, 2016, 31(3): 229-240. doi: 10.1111/mice.12163
|
[16] |
CHENG Hong, ZHENG Nan-ning, QIN Jun-jie. Pedestrian detection using sparse Gabor filter and support vector machine[C]//IEEE. 2005Intelligent Vehicles Symposium. New York: IEEE, 2005: 583-587.
|
[17] |
WU Si, LAGANIRE R, PAYEUR P. Improving pedestrian detection with selective gradient self-similarity feature[J]. Pattern Recognition, 2015, 48(8): 2364-2376. doi: 10.1016/j.patcog.2015.01.005
|
[18] |
ZHANG Shan-shan, BENENSON R, SCHIELE B. Filtered channel features for pedestrian detection[C]//IEEE. 2015IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2015: 1751-1760.
|
[19] |
TIAN Yong-long, LUO Ping, WANG Xiao-gang, et al. Deep learning strong parts for pedestrian detection[C]//IEEE. 2015IEEE International Conference on Computer Vision. New York: IEEE, 2015: 1904-1912.
|
[20] |
UIJLINGS J R R, VAN DE SANDE K E A, GEVERS T, et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104(2): 154-171. doi: 10.1007/s11263-013-0620-5
|
[21] |
DEMIR B, BRUZZONE L. Fast and accurate image classification with histogram based features and additive kernel SVM[C]//IEEE. 2015 IEEE International Geoscience and Remote Sensing Symposium. New York: IEEE, 2015: 2350-2353.
|
[22] |
MAJI S, BERG A C, MALIK J. Efficient classification for additive kernel SVMs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 66-77. doi: 10.1109/TPAMI.2012.62
|
[23] |
孙锐, 陈军, 高隽. 基于显著性检测与HOG-NMF特征的快速行人检测方法[J]. 电子与信息学报, 2013, 35(8): 1921-1926. https://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201308023.htm
SUN Rui, CHEN Jun, GAO Jun. Fast pedestrian detection based on saliency detection and HOG-NMF features[J]. Journal of Electronics and Information Technology, 2013, 35(8): 1921-1926. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201308023.htm
|
[24] |
WU Jian-xin, GEYER C, REHG J M. Real-time human detection using contour cues[C]//IEEE. 2011IEEE International Conference on Robotics and Automation. New York: IEEE, 2011: 860-867.
|
[25] |
曾波波, 王贵锦, 林行刚. 基于颜色自相似度特征的实时行人检测[J]. 清华大学学报: 自然科学版, 2012, 52(4): 571-574. https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201204030.htm
ZENG Bo-bo, WANG Gui-jin, LIN Xing-gang. Color selfsimilarity feature based real-time pedestrian detection[J]. Journal of Tsinghua University: Science and Technology, 2012, 52(4): 571-574. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201204030.htm
|