Browse > Article
http://dx.doi.org/10.15207/JKCS.2019.10.3.039

Modified HOG Feature Extraction for Pedestrian Tracking  

Kim, Hoi-Jun (Dept of Plasma Bio Display, KwangWoon University)
Park, Young-Soo (Ingenium College of Liberal Arts, KwangWoon University)
Kim, Ki-Bong (Department of computer information, Daejeon health institute of technology)
Lee, Sang-Hun (Ingenium College of Liberal Arts, KwangWoon University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.3, 2019 , pp. 39-47 More about this Journal
Abstract
In this paper, we proposed extracting modified Histogram of Oriented Gradients (HOG) features using background removal when tracking pedestrians in real time. HOG feature extraction has a problem of slow processing speed due to large computation amount. Background removal has been studied to improve computation reductions and tracking rate. Area removal was carried out using S and V channels in HSV color space to reduce feature extraction in unnecessary areas. The average S and V channels of the video were removed and the input video was totally dark, so that the object tracking may fail. Histogram equalization was performed to prevent this case. HOG features extracted from the removed region are reduced, and processing speed and tracking rates were improved by extracting clear HOG features. In this experiment, we experimented with videos with a large number of pedestrians or one pedestrian, complicated videos with backgrounds, and videos with severe tremors. Compared with the existing HOG-SVM method, the proposed method improved the processing speed by 41.84% and the error rate was reduced by 52.29%.
Keywords
Histogram of Oriented Gradients(HOG); Object tracking; Computation amount; Background removal; HSV color space;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 T. H. Yoo, G. S. Lee & S. H. Lee. (2012). Window Production Method based on Low-Frequency Detection for Automatic Object Extraction of GrabCut. Journal of Digital Convergence, 10(8), 211-217   DOI
2 H. H. Han, G. S. Lee, J. Y. Lee & S. H. Lee. (2012). Region Segmentation Technique Based on Active Contour for Object Segmentation, Journal of Digital Convergence, 10(3), 167-172.   DOI
3 J. C. Han, B. C. Koo & K. J. Cheoi. (2017). Obstacle Detection and Recognition System for Autonomous Driving Vehicle. Journal of Convergence for Information Technology, 7(6), 229-235. DOI : 10.22156/CS4SMB.2017.7.6.229   DOI
4 M. K. Kwon & H. S. Yang. (2017). A scene search method based on principal character identification using convolutional neural network. Journal of Convergence for Information Technology, 7(2), 31-36. DOI : 10.22156/CS4SMB.2017.7.2.031   DOI
5 A. H. Ahmed, K. Kpalma & A. O. Guedi. (2017, December). Human Detection Using HOG-SVM, Mixture of Gaussian and Background Contours Subtraction. In 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 334-338). IEEE.
6 B. Meus, T. Kryjak & M. Gorgon. (2017, September). Embedded vision system for pedestrian detection based on HOG+SVM and use of motion information implemented in Zynq heterogeneous device. In Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), (pp. 406-411). IEEE.
7 J. H. Kim. (2015). A Study on Tracking in Video Using Modified Particle Filter. Master dissertation. Kwangwoon University, Seoul.
8 N. Dalal & B. Triggs. (2005, June). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 886-893). IEEE.
9 C. Cortes, & V. Vapnik. (1995). Support-vector networks. Machine learning, 20(3), 273-297.   DOI
10 J. H. Baek, J. S. Kim & E. T. Kim. (2014, May). Pedestrian detection with NIR sensor-based HOG-SVM. Institude of Control, Robotics and Systems. (pp. 650-651).
11 Daimler Mono Ped. Detection Benchmark Data set. http://www.gavrila.net
12 S. K. Pyo, G. S. Lee, Y. S. Park, S. H. Lee. (2018). A license plate detection method based on contour extraction that adapts to environmental changes. Journal of the Korea Convergence Society, 9(9), 31-39.   DOI
13 H. Song, Y. Zheng & K. Zhang. (2016). Robust visual tracking via self-similarity learning. Electronics Letters, 53(1), 20-22. DOI : 10.1049/el.2016.3011   DOI
14 J. Y. Kim. (2015). Method on Detection Specific Region Using License Plate Edge Feature In Car Image. Master dissertation. Kwangwoon University, Seoul.
15 J. H. Park. (2017). Improved face detection algorithm using color distribution and shape characteristics. Master dissertation. Kwangwoon University, seoul.
16 M. Cen & C. Jung. (2017). Complex Form of Local Orientation Plane for Visual Object Tracking. IEEE Access, 5, 21597-21604. DOI : 10.1109/ACCESS.2017.2756699   DOI
17 Q. Xie, O. Remil, Y. Guo, M. Wang, M. Wei & J. Wang. (2018). Object Detection and Tracking Under Occlusion for Object-Level RGB-D Video Segmentation. IEEE Transactions on Multimedia. 20(3). 580-592. DOI : 10.1109/TMM.2017.2751965   DOI
18 L. Lan, X. Wang, S. Zhang, D. Tao, W. Gao & T. S. Huang. (2018). Interacting Tracklets for Multi-Object Tracking. IEEE Transactions on Image Processing. 27(9). 4585-4597 DOI : 10.1109/TIP.2018.2843129   DOI
19 R. Yu, I.. Cheng, B. Zhu, S. Bedmutha & A. Basu. (2018). Adaptive Resolution Optimization and Tracklet Reliability Assessment for Efficient Multi-Object Tracking. IEEE Transactions on Circuits and Systems for Video Technology. 28(7). 1623-1633. DOI : 10.1109/TCSVT.2017.2668278   DOI