38 3 Vol. 38, No. 3 2012 3 ACTA AUTOMATICA SINICA March, 2012 1 1 1, (Adaboost) (Support vector machine, SVM). (Four direction features, FDF) GAB (Gentle Adaboost) (Entropy-histograms of oriented gradients, EHOG),. EHOG,,., EHOG, Adaboost. DOI,,, GAB,, 10.3724/SP.J.1004.2012.00375 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] 2011-07-11 2011-09-14 Manuscript received July 11, 2011; accepted September 14, 2011 (40721001, 40830530),, Supported by National Natural Science Foundation of China (40721001, 40830530), 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 1. 430079 1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079 (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,,,.,
376 38 [9]. 1. Fig. 1 1 1.1 1.1.1 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 2. 1.1.2 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 : 377 Block, 3, Block, Block. Fig. 3 3 Block The comparison of the Block entropies of positive and negative samples 3 Block, EHOG,,. 1.2 1.2.1 FDF GAB Adaboost, Boosting, Freund [6] 1995. 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) = 0. 2. 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 = 1. 3. : ( 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) = 1. 4. i = 1. 5. While F (i) > F target 5.1. i = i + 1, F (1) = F (i 1), ni = 0, ni, GAB. 5.2. While F (i) > f a) ni = ni + 1; b) ni GAB DP, F P ; c) DP > d, ; d) ( ni = 1),,,. 5.3.. 5.4.,,,, i, i 1. 6.,. 1.2.2 SVM (SVM) Vapnik
378 38 [15], Vapnik,,, [16 17].,,, [18]. SVM. 2 4, [19] Haar, [20] HOG SVM, [21] Haarlike HOG, [22] Edgelet. ROC,,,,. 4,,. FDF GAB OpenCV Haar 5. 2.1 Matlab 2009a, : Intel Core i3 540 CPU 3.07 GHz, 3 G. INRIA, INRIA, 128 64, INRIA. 2.2 FDF FDF GAB, 40, 1 000 : 1 000, 24 h, 19 ; SVM Libsvm, 2 000 : 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 %.
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 288 488 479 98 5.68 10 2 EHOG 288 488 479 98 5.11 10 2 2.4 FDF GAB OpenCV Haar INRIA 1 000 1 500, OpenCV Haar [19], : nstages 20, w 32, h 64, minhitrate 0.995, maxfalsealarm 0.5. Haar FDF, INRIA. 7, 2. 7, Haar,,,. 2, Table 2 2 7 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. 640 480, 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) 288 488 12 463 95 5.53 10 2 0.19 288 488 2 479 98 8.94 10 2 0.48 Haar 288 488 2 278 57 8.36 10 2 0.25 Haar 288 488 12 117 24 9.79 10 3 0.03
380 38 2.5,, 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, 2010 3 Viola P, Jones M J. Robust real-time face detection. International Journal of Computer Vision, 2004, 57(2): 137 154 4 Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Computer So-
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