Two-stage Pedestrian Detection Based on Multiple Features and Machine Learning

Transcription

1 38 3 Vol. 38, No ACTA AUTOMATICA SINICA March, , (Adaboost) (Support vector machine, SVM). (Four direction features, FDF) GAB (Gentle Adaboost) (Entropy-histograms of oriented gradients, EHOG),. EHOG,,., EHOG, Adaboost. DOI,,, GAB,, /SP.J Two-stage Pedestrian Detection Based on Multiple Features and Machine Learning CHONG Yan-Wen 1 KUANG Hu-Lin 1 LI Qing-Quan 1 Abstract A two-stage detection method based on Adaboost and support vector machine (SVM) is proposed for the pedestrian detection problem in a single image, which uses the combination of coarse level and fine level detection to improve the accuracy of the detector. The coarse level pedestrian detector makes use of the four direction features (FDF) and the gentle Adaboost (GAB) cascade training; the fine level pedestrian detector uses entropy-histograms of oriented gradients (EHOG) as features and the SVM as classifier. The proposed EHOG features considering entropy and the distribution of chaos have the ability to distinguish between the pedestrians and the objects similar to people. Experimental results show that the proposed two-stage pedestrian detection method with the combination of the coarsefine level and EHOG feature can accurately detect upright bodies with different postures in the complex background, at the same time the precision is better than the classic Adaboost methods. Key words Four direction features (FDF), entropy-histograms of oriented gradients (EHOG), Adaboost, gentle Adaboost (GAB) cascade, support vector machine (SVM), two-stage detection, [1 2]., (Haar) [3], (Histograms of oriented gradients, HOG) [4 5], (Adaboost) [6] Manuscript received July 11, 2011; accepted September 14, 2011 ( , ),, Supported by National Natural Science Foundation of China ( , ), China Postdoctoral Science Foundation Funded Project, and State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing Special Research Funding, Wuhan University Recommended by Associate Editor LIU Cheng-Lin State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan (Neural network) [7] (Support vector machine, SVM) [8]. 1., (Four direction features, FDF) GAB (Gentle Adaboost) ; (Entropy-histograms of oriented gradients, EHOG), SVM.,, (Regions of interest, ROIs), ROIs, ROIs,,,.,

2 [9]. 1. Fig FDF The flow chart of the proposed pedestrian detection method FDF [10 11], Haar HOG,,. FDF :,, FDF ; FDF, FDF. FDF : Sd(x) = I(x) Gd B (1) Sd(x) FDF, I(x),, Gd, d D, D = {0, 45, 90, 135 }, 2 (a). B 2. FDF, M N ( 4 4), M N, FDF. FDF EHOG (HOG),, Dalal 2005 [4]. HOG, [12]. HOG Haar FDF,,., HOG,. HOG,, (, ).,,,, EHOG (Entropy-HOG). HOG P,, Block, HOG, EHOG, Block, 9 : Fig. 2 E = 2 9 P i log P i (2) i=1 FDF The extraction process of FDF feature MIT

3 3 : 377 Block, 3, Block, Block. Fig. 3 3 Block The comparison of the Block entropies of positive and negative samples 3 Block, EHOG,, FDF GAB Adaboost, Boosting, Freund [6] Adaboost Discrete Adaboost (DAB), Real Adaboost (RAB) Gentle Adaboost (GAB). DAB,. DAB, [13]. GAB. GAB, : h(x) = P w (y = 1 x) P w (y = 1 x) (3), P w (y = 1 x), P w (y = 1 x) x,. GAB [9, 14] : 1. : w i = 1/N, i = 1,, N, F (x) = For t = 1,, T a) y x f t (x); b) F (x) F (x) + f t (x); c) w i w i exp( y i f t (x i )), i = 1,, N,, w i i = : ( T ) sgn(f (x)) = sgn f t (x) (4) t=1, f t (x) = P w (y = 1 x) P w (y = 1 x), w i, N, y i, 1, 1, T, F (x). FDF GAB,,,,,,., FDF,,. : 1. d, F target, f. 2., ( ), FDF. 3. D(1) = 1, F (1) = i = While F (i) > F target 5.1. i = i + 1, F (1) = F (i 1), ni = 0, ni, GAB While F (i) > f a) ni = ni + 1; b) ni GAB DP, F P ; c) DP > d, ; d) ( ni = 1),,, ,,,, i, i 1. 6., SVM (SVM) Vapnik

4 [15], Vapnik,,, [16 17].,,, [18]. SVM. 2 4, [19] Haar, [20] HOG SVM, [21] Haarlike HOG, [22] Edgelet. ROC,,,,. 4,,. FDF GAB OpenCV Haar Matlab 2009a, : Intel Core i3 540 CPU 3.07 GHz, 3 G. INRIA, INRIA, , INRIA. 2.2 FDF FDF GAB, 40, : 1 000, 24 h, 19 ; SVM Libsvm, : 2 000, 400 s. FDF GAB ROC,, 4. 4 Fig. 4 FDF GAB ROC Comparison of the ROC curves of the GAB cascade classifier based on FDF and classifiers gained by other features 5 Fig. 5 FDF GAB Haar Comparison of the feature numbers in each stage of the GAB cascade classifier based on FDF and the Haar cascade classifier 5, Haar, Haar, FDF GAB. 2.3 EHOG HOG EHOG, SVM, 6 (a)., HOG, SVM, 6 (b)., EHOG HOG, EHOG., INRIA, 288, HOG EHOG, 1.2, 12, 1, EHOG HOG 10 %.

5 3 : 379 (a) OpenCV Haar (a) The detection results of the OpenCV Haar cascade method Fig. 6 6 EHOG HOG Comparison of EHOG detection results and HOG detection results 1 HOG EHOG Table 1 Statistic comparison of EHOG detection results with HOG detection results (%) (%) HOG EHOG FDF GAB OpenCV Haar INRIA , OpenCV Haar [19], : nstages 20, w 32, h 64, minhitrate 0.995, maxfalsealarm 0.5. Haar FDF, INRIA. 7, 2. 7, Haar,,,. 2, Table Fig. 7 (b) (b) The detection results of the method in the paper OpenCV Haar Comparison of the detection results of the method in the paper and the OpenCV Haar cascade method Haar Haar., FDF Haar , 2,, OpenCV Haar,,, Matlab,. FDF Haar Statistic comparison of the detection results of the method in the paper and the OpenCV Haar cascade method (%) (%) (s) Haar Haar

6 ,, ROC,, 8. [21] FBD, Haar-like HOG,, Edgelet,. [23] Multi-scale orientation (MSO ),, Adaboost, SVM. Fig. 9 9 Example of the final detection results for the method in the paper 8 ROC Fig. 8 Comparison of thre ROC curves of some two-stage detection 8 ROC,,,,. 2.6,, ROIs, ROIs, ROIs,. 9. Adaboost, ( ) ( ),,,,. 3, Adaboost SVM,,, ( ) ( ),,,,,. : 1) HOG, EHOG, ; 2) FDF,, FDF Haar, ; 3),. : 1), ; 2),,,, ; 3). References 1 Liu Wen-Jing. Human Detection System Based on AD- ABOOST Algorithm [Master dissertation], Jilin University, China, 2009 (. ADABOOST [ ],,, 2009) 2 Geronimo D. A Global Approach to Vision-Based Pedestrian Detection for Advanced Driver Assistance Systems [Ph. D. dissertation], Universitat Autonoma de Barcelona, Spain, Viola P, Jones M J. Robust real-time face detection. International Journal of Computer Vision, 2004, 57(2): Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Computer So-

7 3 : 381 ciety Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, Zeng Chun, Li Xiao-Hua, Zhou Ji-Liu. Pedestrian detection based on HOG of ROI. Computer Engineering, 2009, 35(24): (,,.., 2009, 35(24): ) 6 Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the 2nd European Conference on Computational Learning Theory. Barcelona, Spain: Springer, Zhao L, Thorpe C. Stereo- and neural network-based pedestrian detection. IEEE Transactions on Intelligent Transportation Systems, 2000, 1(3): Cheng H, Zheng N N, Qin J J. Pedestrian detection using sparse Gabor filter and support vector machine. In: Proceedings of the IEEE Intelligent Vehicles Symposium. Las Vegas, USA: IEEE, Tian Guang. Study of Visual Based Pedestrian Detection and Tracking Algorithm [Ph. D. dissertation], Shanghai Jiao Tong University, China, 2007 (. [ ],,, 2007) 10 Soga M, Hiratsuka S, Fukamachi H, Ninomiya Y. Pedestrian detection for a near infrared imaging system. In: Proceedings of the 11th IEEE International Conference on Intelligent Transportation Systems. Beijing, China: IEEE, Iwata K, Hongo H, Yamamoto K, Niwa Y. Robust facial parts detection by using four directional features and relaxation matching. In: Proceedings of the 7th International Conference on Knowledge-Based Intelligent Information and Engineering Systems. Oxford, UK: Springer, Zhou Ke. Research and Implementation of HOG Based Human Detection of Image [Master dissertation], Huazhong University of Science and Technology, China, 2008 (. HOG [ ],,, 2008) 13 Wang Jian-Hong, Zhang Pin-Zheng, Luo Li-Min. Improved fast pedestrian detection method. Computer Engineering and Applications, 2009, 45(28): (,,.., 2009, 45(28): ) 14 Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. The Annals of Statistics, 2000, 28(2): Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer, Zhang Xiao-Chuan. Human-Targets Tracking Based on Histogram of Oriented Gradient and Support Vector Machine [Master dissertation], Dalian University of Technology, China, 2009 (. [ ],,, 2009) 17 Pan Feng, Wang Xuan-Yin. Support vector machine-based human cetection under complex background. Journal of Image and Graphics, 2005, 10(2): (,.., 2005, 10(2): ) 18 Hu Jian-Hua, Xu Jian-Jian. Detection and recognition of vehicle and pedestrian in intelligent traffic surveillance. Electronic Measurement Technology, 2007, 30(1): 16 17, 71 (,.., 2007, 30(1): 16 17, 71) 19 Viola P A, Jones M J. Rapid object detection using a boosted cascade of simple feature. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai, USA: IEEE, Zhang W, Zelinsky G, Samaras D. Real-time accurate object detection using multiple resolutions. In: Proceedings of the 11th IEEE International Conference on Computer Vision. Rio de Janeiro, Brazil: IEEE, Li Z, Wei Z Q, Yin B, Ji X P, Shan R B. Pedestrian detection based on a new two-step framework. In: Proceedings of the 2nd International Workshop on Education Technology and Computer Science. Wuhan, China: IEEE, Wu B, Nevatia R. Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors. In: Proceedings of the 10th IEEE International Conference on Computer Vision. Beijing, China: IEEE, Ye Q X, Jiao J B, Zhang B C. Fast pedestrian detection with multi-scale orientation features and two-stage classifiers. In: Proceedings of the 17th IEEE International Conference on Image Processing. Hong Kong, China: IEEE, ,.. apollobest@126.com (CHONG Yan-Wen Associate professor at the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMRS), Wuhan University. His research interest covers computer vision, video processing, and pattern recognition. Corresponding author of this paper.).. kuanghulin@sina.com (KUANG Hu-Lin Master student at the School of Electronic Information, Wuhan University. His research interset covers computer vision and pedestrian detection.).,,. qqli@whu.edu.cn (LI Qing-Quan Professor at the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMRS), Wuhan University. His research interest covers photogrammetry and remote sensing, theory and applications of geographic information systems, and location based services and intelligent transportation systems.)