Abstract
The existing pedestrian detection algorithms are not robust in the case of noise, obstruction and illumination change. To solve the problem, we propose a pedestrian detection algorithm combining the edge features of color image with the features of Histogram of Oriented Gradient on Depth image (referred to as the HOD features). The algorithm describes overall structural features of pedestrians by using shearlet transform to extract their edge features from color images, and obtains local edge features of corresponding depth images by generating HOD features. The overall structural features and local edge features are combined to form new feature descriptors to train a SVM (support vector machine) classifier. Due to the full integration of the two types of features, the algorithm shows significant advantages in pedestrian detection in the case of interfering factors such as noise, obstruction, illumination, and similar colors. The experimental results show that the detection accuracy rate of this algorithm is 15% higher than that of other algorithms when the false-positive rate is 0.1.
Similar content being viewed by others
REFERENCES
Zhang, L., Lin, L., Liang, X., et al., Is faster R-CNN doing well for pedestrian detection?, European Conference on Computer Vision, Cham, 2016, pp. 443–457.
Combs, T.S., Sandt, L.S., Clamann, M.P., et al., Automated vehicles and pedestrian safety: Exploring the promise and limits of pedestrian detection, Am. J. Prev. Med., 2019, vol. 56, no. 1, pp. 1–7.
Zhang, S., Benenson, R., Omran, M., et al., How far are we from solving pedestrian detection?, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1259–1267.
Zhang, S., Benenson, R., and Schiele, B., Filtered channel features for pedestrian detection, CVPR, 2015, vol. 1, no. 2, p. 4.
Li, J., Liang, X., Shen, S.M., et al., Scale-aware fast R-CNN for pedestrian detection, IEEE Trans. Multimedia, 2017, vol. 20, no. 4, pp. 985–996.
Cai, Z., Saberian, M., and Vasconcelos, N., Learning complexity-aware cascades for deep pedestrian detection, Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 3361–3369.
Mao, J., Xiao, T., Jiang, Y., et al., What can help pedestrian detection?, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 3127–3136.
Paisitkriangkrai, S., Shen, C., and Hengel, A., Pedestrian detection with spatially pooled features and structured ensemble learning, IEEE Trans. Pattern Anal. Mach. Intell., 2015, vol. 38, no. 6, pp. 1243–1257.
Li, J., Gong, W., Li, W., et al., Robust pedestrian detection in thermal infrared imagery using the wavelet transform, Infrared Phys. Technol., 2010, vol. 53, no. 4, pp. 267–273.
Dalal, N. and Triggs, B., Histograms of oriented gradients for human detection, International Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, vol. 1, pp. 886–893.
Freund, Y. and Schapire, R.E., A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci., 1997, vol. 55, no. 1, pp. 119–139.
Cheng, H., Zheng, N., and Qin, J., Pedestrian detection using sparse Gabor filter and support vector machine, IEEE Proceedings. Intelligent Vehicles Symposium, 2005, pp. 583–587.
Mu, Y., Yan, S., Liu, Y., et al., Discriminative local binary patterns for human detection in personal album, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1–8.
Spinello, L. and Arras, K.O., People detection in RGB-D data, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, pp. 3838–3843.
Wang, X., Han, T.X., and Yan, S., An HOG-LBP human detector with partial occlusion handling, 2009 IEEE 12th international conference on computer vision, 2009, pp. 32–39.
Tuzel, O., Porikli, F., and Meer, P., Pedestrian detection via classification on Riemannian manifolds, IEEE Trans. Pattern Anal. Mach. Intell., 2008, vol. 30, no. 10, pp. 1713–1727.
Wang, N., Gong, X., and Liu, J., A new depth descriptor for pedestrian detection in RGB-D images, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 2012, pp. 3688–3691.
Yi, S., Labate, D., Easley, G.R., et al., A shearlet approach to edge analysis and detection, IEEE Trans. Image Process., 2009, vol. 18, no. 5, pp. 929–941.
Arunachalam, M. and Royappan Savarimuthu, S., An efficient and automatic glioblastoma brain tumor detection using shift invariant shearlet transform and neural networks, Int. J. Imaging Syst. Technol., 2017, vol. 27, no. 3, pp. 216–226.
Soni, R., Kumar, B., and Chand, S., Text detection and localization in natural scene images based on text awareness score, Appl. Intell., 2018, vol. 49, pp. 1376–1405.
Spinello, L., Luber, M., and Arras, K.O., Tracking people in 3D using a bottom-up top-down people detector, International Conference on Robotics and Automation (ICRA), Shanghai, 2011, pp. 1304–1310.
Funding
This work has been supported in part by Scientific and Technological Innovation Team of Shanxi Province (no. 201705D131025) and Collaborative Innovation Center of Internet+3D Printing in Shanxi Province (201708).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
The authors declare that there is no conflict of interests.
About this article
Cite this article
Xiong Zhang, Shangguan, H., Ning, A. et al. Pedestrian Detection with EDGE Features of Color Image and HOG on Depth Images. Aut. Control Comp. Sci. 54, 168–178 (2020). https://doi.org/10.3103/S0146411620020108
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S0146411620020108