Abstract：The detection accuracy of the Deformable Part Model (DPM) algorithm is high in the field of pedestrian detection, however, in the two steps of feature extraction and pedestrian location, the computation is too large, which leads to the slow detection speed and the deformable part model algorithm can not be used in real time pedestrian detection. To solve the problems, a deformable Part Model with Branch and Bound (BB) algorithm and Cascaded Detection (CD) algorithm (BBCDPM) was proposed. First, the Histogram of Oriented Gradients (HOG) feature was selected to describe human target to generate characteristic pyramid. Then, the deformable part model was modeled, and the Latent Support Vector Machine (LSVM) was used to train the model. In order to increase the accuracy of pedestrian detection, the part model of traditional deformation part model algorithm was increased from 5 to 8 parts. Finally, the cascade detection algorithm was used to simplify detection model, then the maximum value was found by combining with the branch and bound algorithm, and a lot of impossible object assumptions were removed, so the pedestrian target location and detection were completed. The experimental results on INRIA dataset show that, compared with the traditional DPM algorithm, the proposed algorithm improves the accuracy rate by 12 percentage points and significantly accelerates pedestrian detection and recognition.
柴恩惠, 智敏. 融合分支定界的可变形部件模型的行人检测[J]. 计算机应用, 2017, 37(7): 2003-2007.
CHAI Enhui, ZHI Min. Pedestrian detection based on deformable part model with branch and bound. Journal of Computer Applications, 2017, 37(7): 2003-2007.
[1] FELZENSZWALB P F, MCALLESTER D, RAMANAN D. A discriminatively trained, multiscale, deformable part model[C]//Proceedings of 2008 IEEE Computer Vision and Pattern Recognition Workshops. Washington, DC:IEEE Computer Society, 2008:1-8. [2] 刘琦,黄咨,胡福乔,等.基于GPU的卷积检测模型加速[J].计算机应用与软件,2016,33(5):226-230.(LIU Q, HUANG Z, HU F Q, et al. Convolution-based detection models acceleration based on GPU[J]. Computer Applications and Software, 2016, 33(5):226-230.) [3] PEDERSOLI M, VEDALDI A, GONZAIEZ J, et al. A coarse-to-fine approach for fast deformable object detection[J]. Pattern Recognition, 2015, 48(5):1844-1853. [4] SONG H, GIRSHICK R, ZICKLER S, et al. Generalized sparselet models for real-time multiclass object recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(5):1001-1012. [5] 甘鹏坤,陶凌,龙伟.基于可变形部件模型及稀疏特征的行人检测[J].深圳大学学报(理工版),2015,32(6):563-570.(GAN P K, TAO L, LONG W. Cascade pedestrian detection based on the deformable part models and histograms of sparse codes features[J]. Journal of Shenzhen University (Science and Engineering), 2015, 32(6):563-570.) [6] QIANG Z, SHAI A, MEI C Y, et al. Fast human detection using a cascade of histograms of oriented gradients[C]//Proceedings of 2006 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Washington, DC:IEEE Computer Society, 2006:1491-1498. [7] 张国云,郭龙源,吴健辉,等.计算机视觉与图像识别[M].北京:科学出版社,2012:165.(ZHANG G Y, GUO L Y, WU J H, et al. Computer Vision and Image Recognition[M]. Beijing:Science Press, 2012:165.) [8] 田娟,郑郁正.模板匹配技术在图像识别中的应用[J].传感器与微系统,2008,27(1):112-114.(TIAN J, ZHENG Y Z. Application of template matching technique in image recognition[J]. Transducer and Microsystem Technologies, 2008, 27(1):112-114.) [9] 王宁波.基于RGB-D的行人检测[D].杭州:浙江大学,2013:5-7.(WANG N B. Pedestrian detection based on RGB-D[D]. Hangzhou:Zhejiang University, 2013:5-7.) [10] ROBERT E S, YORAM S. Improved boosting algorithms using confidence-rated prediction[J]. Machine Learning, 1999, 37(3):297-336. [11] 程和生,胡幸福.基于HOG和SVM的人体检测技术在静态图像中的研究[J].仪器仪表用户,2012,19(5):20-23.(CHENG H S, HU X F. The study on the human detection technology in the static image based on HOG and SVM[J]. Instrumentation Customer, 2012, 19(5):20-23.) [12] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Proceedings of the 2005 Computer Vision and Pattern Recognition Workshops. Washington, DC:IEEE Computer Society, 2005:886-893. [13] FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D. et al. Object detection with discriminatively trained part based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9):1627-1645. [14] FELZENSZWALB P F, HUTTENLOCHER D P. Distance transforms of sampled functions[J]. Theory of Computing, 2012, 8:415-428. [15] FELZENSZWALB P F, HUTTENLOCHER D P. Pictorial struc-tures for object recognition[J]. International Journal of Computer Vision, 2005, 61(1):55-79. [16] 赵宇.基于动态规划的检测前跟踪算法研究[D].西安:西安电子科技大学,2012:9-12.(ZHAO Y. Research on tracking algorithm based on dynamic programming[D]. Xi'an:Xidian University, 2012:9-12.) [17] 张闯.基于可变形部件模型的人体级联检测[D].大连:大连海事大学,2014:33-34.(ZHANG C. Human cascade detection based on deformable component model[D]. Dalian:Dalian Maritime University, 2014:33-34.) [18] GIRSHICK R B. From rigid templates to grammars:object detection with structured models[D]. Chicago, IL:The University of Chicago, 2012:1-129.
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