Pedestrian detection algorithm based on video sequences and laser point cloud

doi:10.1007/s11704-014-3413-2

Front. Comput. Sci.

2015, Vol. 9

Issue (3) : 402-414 https://doi.org/10.1007/s11704-014-3413-2

RESEARCH ARTICLE

Pedestrian detection algorithm based on video sequences and laser point cloud

Hui LI^1,^*(

),Yun LIU¹,Shengwu XIONG²,Lin WANG²

1. School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
2. School of Computer Science, Wuhan University of Technology,Wuhan 430070, China

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Abstract

Pedestrian detection is a critical problem in the field of computer vision. Although most existing algorithms are able to detect pedestrians well in controlled environments, it is often difficult to achieve accurate pedestrian detection from video sequences alone, especially in pedestrian-intensive scenes wherein pedestrians may cause mutual occlusion and thus incomplete detection. To surmount these difficulties, this paper presents pedestrian detection algorithm based on video sequences and laser point cloud. First, laser point cloud is interpreted and classified to separate pedestrian data and vehicle data. Then a fusion of video image data and laser point cloud data is achieved by calibration. The region of interest after fusion is determined using feature information contained in video image and three-dimensional information of laser point cloud to remove false detection of pedestrian and thus to achieve pedestrian detection in intensive scenes. Experimental verification and analysis in video sequences demonstrate that fusion of two data improves the performance of pedestrian detection and has better detection results.

Keywords computer vision pedestrian detection video sequences laser point cloud

Corresponding Author(s): Hui LI

Issue Date: 18 May 2015

Cite this article:

Shengwu XIONG,Lin WANG,Hui LI, et al. Pedestrian detection algorithm based on video sequences and laser point cloud[J]. Front. Comput. Sci., 2015, 9(3): 402-414.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-014-3413-2
https://academic.hep.com.cn/fcs/EN/Y2015/V9/I3/402

1	Enzweiler M, Gavrila D M. Monocular pedestrian detection: survey and experiments. IEEE Transactions on Pattern Analysis andMachine Intelligence, 2009, 31(12): 2179-2195
2	Cao X B, Wang Z, Yan P K, Li X L. Transfer learning for pedestrian detection. Neurocomputing, 2013, 100(16): 51-57 https://doi.org/10.1016/j.neucom.2011.12.043
3	Yan J J, Zhang X C, Lei Z, Liao S C, Li S Z. Robust multiresolution pedestrian detection in traffic scenes. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2013, 3033-3040
4	Ouyang W L, Wang X G. Single-pedestrian detection aided by multipedestrian detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2013, 3198-3205
5	Tian H, Duan Z, Abraham A, Liu H B. A novel multiplex cascade classifier for pedestrian detection. Pattern Recognition Letters, 2013, 34(14): 1687-1693 https://doi.org/10.1016/j.patrec.2013.04.015
6	Wang J T, Chen D B, Chen H Y, Yang J Y. On pedestrian detection and tracking in infrared videos. Pattern Recognition Letters, 2012, 33(6): 775-785 https://doi.org/10.1016/j.patrec.2011.12.011
7	Barbu T. Multiple object detection and tracking in sonar movies using an improved temporal differencing approach and texture analysis. UPB Scientific Bulletin, Series A: Applied Mathematics and Physics, 2012, 74(2): 27-40
8	Karasulu B, Korukoglu S. Moving object detection and tracking by using annealed background subtraction method in videos: performance optimization. Expert Systems with Applications, 2012, 39(1): 33-43 https://doi.org/10.1016/j.eswa.2011.06.040
9	Doyle D D, Jennings A L, Black J T. Optical flow background estimation for real-time pan/tilt camera object tracking. Measurement, 2014, 48: 195-207 https://doi.org/10.1016/j.measurement.2013.10.025
10	Paisitkriangkrai S, Shen C H, Zhang J. Fast pedestrian detection using a cascade of boosted covariance features. IEEE Transactions on Circuits and Systems for Video Technology, 2008, 18(8): 1140-1151 https://doi.org/10.1109/TCSVT.2008.928213
11	Shih P, Liu C. Face detection using distribution-based distance and support vector machine. In: Proceedings of International Conference on Computational Intelligence and Multimedia Applications. 2005, 327-332
12	Papageorgiou C, Poggio T. A trainable system for object detection. International Journal of Computer Vision, 2000, 38(1): 15-33 https://doi.org/10.1023/A:1008162616689
13	Szarvas M, Yoshizawa A, Yamamoto M, Ogata J. Pedestrian detection with convolutional neural networks. In: Proceedings of the 2005 IEEE Intelligent Vehicles Symposium. 2005, 224-229 https://doi.org/10.1109/IVS.2005.1505106
14	Viola P, Jones M J, Snow D. Detecting pedestrians using patterns of motion and appearance. In: Proceedings of IEEE International Conference on Computer Vision. 2003, 734-741 https://doi.org/10.1109/ICCV.2003.1238422
15	Viola P, Jones M. Robust real-time object detection. International Journal of Computer Vision, 2003, 57(2): 137-154 https://doi.org/10.1023/B:VISI.0000013087.49260.fb
16	Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 886-893 https://doi.org/10.1109/cvpr.2005.177
17	Felzenszwalb P F, Girshick R B, McAllester D, Ramanan D. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645 https://doi.org/10.1109/TPAMI.2009.167
18	Schneider S, Himmelsbach M, Luettel T, Wuensche H J. Fusing vision and LIDAR-synchronization, correction and occlusion reasoning. In: Proceedings of the IEEE Intelligent Vehicles Symposium. 2010, 21-24 https://doi.org/10.1109/ivs.2010.5548079
19	Cui J S, Zha H B, Zhao H J, Shibasaki R. Laser-based detection and tracking of multiple people in crowds. Computer Vision and Image Understanding, 2007, 106(2-3): 300-312 https://doi.org/10.1016/j.cviu.2006.07.015
20	Spinello L, Arras K O, Triebel R, Siegwart R. A layered approach to people detection in 3D range data. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence. 2010, 1625-1630
21	Kidono K, Miyasaka T, Watanabe A, Naito T, Miura J. Pedestrian recognition using high-definition LIDAR. Intelligence Vehicles Symposium, 2011, 405-410 https://doi.org/10.1109/ivs.2011.5940433
22	Rottensteiner F, Trinder J, Clode S, Kubik K. Fusing airborne laser scanner data and aerial imagery for the automatic extraction of buildings in densely built up areas. International Archives of Photogrammetry and Remote Sensing, 2004, 35: 512-517
23	Ronnholm P, Honkavaara E, Litkey P, Hyyppa H, Hyyppa J. Integration of Laser Scanning and Photogrammetry. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2007, 36, 355-362
24	Gerke M, Xiao J. Fusion of airborne laser scanning point clouds and images for supervised and unsupervised scene classification. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 78-92 https://doi.org/10.1016/j.isprsjprs.2013.10.011
25	Cui J S, Zha H B, Zhao H J, Shibasaki R. Multi-modal tracking of people using laser scanners and video camera. Image and Vision Computing, 2008, 26(2): 240-252 https://doi.org/10.1016/j.imavis.2007.05.005
26	Gong X J, Lin Y, Liu J L. Extrinsic calibration of a 3D LIDAR and a camera using a trihedron [J]. Optics and Lasers in Engineering, 2013, 51(4): 394-401 https://doi.org/10.1016/j.optlaseng.2012.11.015
27	Glennie C, Lichti D D. Static calibration and analysis of the Velodyne HDL-64E S2 for high accuracy mobile scanning. Remote Sensing, 2010, 2(6): 1610-1624 https://doi.org/10.3390/rs2061610

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