The traditional feature fusion method based on HOG and LBP loses much spectral information, and it is more sensitive to noise. The original LBP algorithm has poor robustness to uneven illumination changes and poor rotation invariance to texture features. In order to overcome these shortcomings of the method, this paper proposes a pedestrian detection algorithm based on the feature fusion of CLBC and HOG. First, the CLBC feature of the original image is calculated, and the HOG feature based on the CLBC texture feature spectrum is calculated. The HOG feature of the original image is then calculated to extract the edge feature of the image. Then three features of the image are fused to describe the image, and after that we use principal component analysis to reduce the feature dimension. Finally, the detection of the pedestrian is realized by using the HIKSVM classifier. In this paper, experiments are carried out in Caltech pedestrian database and INRIA pedestrian database to verify the effectiveness of the proposed algorithm. The final experimental results show that the proposed algorithm improves the accuracy of pedestrian detection.
Extended HOG-CLBC for pedstrain detection
First published at:Jul 27, 2018
 Su S Z, Li S Z, Chen S Y, et al. A survey on pedestrian detection[J]. Acta Electronica Sinica, 2012, 40(4): 814–820.
苏松志, 李绍滋, 陈淑媛, 等. 行人检测技术综述[J]. 电子学报, 2012, 40(4): 814–820.
 Benenson R, Omran M, Hosang J, et al. Ten years of pedestrian detection, what have we learned?[C]//Proceedings ofComputer Vision - ECCV 2014 Workshops, 2014: 613–627.
 Dollar 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.
 Ouyang W L, Wang X G. Joint deep learning for pedestrian detection[C]//Proceedings of IEEE International Conference on Computer Vision, 2013: 2056–2063.
 Zeng X Y, Ouyang W L, Wang X G. Multi-stage contextual deep learning for pedestrian detection[C]//Proceedings of IEEE International Conference on Computer Vision, 2013: 121–128.
 Luo P, Tian Y L, Wang X G, et al. Switchable deep network for pedestrian detection[C]//Proceedings of Conference on Computer Vision and Pattern Recognition, 2014: 899–906.
 Zeng X Y, Ouyang W L, Wang M, et al. Deep learning of scene-specific classifier for pedestrian detection[C]//Fleet D, Pajdla T, Schiele B, et al. Computer Vision – ECCV 2014. Cham: Springer, 2014: 472–487.
 Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005: 886–893.
 Ojala T, Pietikäinen M, Harwood I. A comparative study of texture measures with classification based on featured distributions[J]. Pattern Recognition, 1996, 29(1): 51–59.
 Tribak H, Moughyt S, Zaz Y, et al. Remote QR code recog-nition based on HOG and SVM classifiers[C]//Proceedings of International Conference on Informatics and Computing, 2017: 137–141.
 Wang X Y, Han T X, Yan S C. An HOG-LBP human detector with partial occlusion handling[C]//Proceedings of IEEE 12th International Conference on Computer Vision, 2010: 32–39.
 Fan G J, Li B, Mu W Q, et al. HOGG: Gabor and hog-based human detection[C]//Proceedings of 8th International Conference on Information Technology in Medicine and Education, 2016: 562–566.
 Peng Z R, Zhao M R, Yang W M, et al. Improved multispectral aggregate channel for pedestrian detection[J]. Op-to-Electronic Engineering, 2017, 44(9): 882–887.
彭志蓉, 赵美蓉, 杨伟明, 等. 改进的多光谱聚合通道行人检测[J]. 光电工程, 2017, 44(9): 882–887.
 Zhao L H, Liu F, Wang Y J. Face recognition based on LBP and genetic algorithm[C]//Proceedings of Chinese Control and Decision Conference, 2016: 1582–1587.
 Liu Y C, Huang S S, Lu C H, et al. Thermal pedestrian detection using block LBP with multi-level classifier[C]//Proceedings of International Conference on Applied System Innovation, 2017: 602–605.
 Guo Z H, Zhang L, Zhang D. A completed modeling of local binary pattern operator for texture classification[J]. IEEE Transactions on Image Processing, 2010, 19(6): 1657–1663.
 Cheng W L, Wang X J, Wan Z J, et al. Research and imple-mentation of target tracking algorithm in compression domain on miniaturized DSP platform[J]. Opto-Electronic Engineering, 2017, 44(10): 972–982.
程卫亮, 王向军, 万子敬, 等. 压缩域目标跟踪算法在小型化DSP平台上的研究与实现[J]. 光电工程, 2017, 44(10): 972–982.
 Ji L P, Ren Y, Liu G S, et al. Training-based gradient lbp feature models for multiresolution texture classification[J]. IEEE Transactions on Cybernetics, 2017, pp(99): 1–14.
 Zhao Y, Huang D S, Jia W. Completed local binary count for rotation invariant texture classification[J]. IEEE Transactions on Image Processing, 2012, 21(10): 4492–4497.
 Maji S, Berg A C, Malik J. Classification using intersection kernel support vector machines is efficient[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008: 1–8.
 Wojek C, Schiele B. A performance evaluation of single and multi-feature people detection[C]//Proceedings of the 30th DAGM symposium on Pattern Recognition, 2008: 82–91.
 Watanabe T, Ito S. Two co-occurrence histogram features using gradient orientations and local binary patterns for pedestrian detection[C]//Proceedings of 2nd IAPR Asian Conference on Pattern Recognition, 2013: 415–419.
 Viola P, Jones M J. Robust real-time face detection[J]. International Journal of Computer Vision, 2004, 57(2): 137–154.
National Natural Science Foundation of China (51774281), High Level Talent Training Project of "Top Six Talents" in Jiangsu Province
Get Citation: Cheng Deqiang, Tang Shixuan, Feng Chenchen, et al. Extended HOG-CLBC for pedstrain detection[J]. Opto-Electronic Engineering, 2018, 45(8): 180111.