Automatic Samples Selection Using Histogram of Oriented Gradients (HOG) Feature Distance

  • Inzar Salfikar Politeknik Elektronika Negeri Surabaya, Indonesia
  • Indra Adji Sulistijono Politeknik Elektronika Negeri Surabaya, Indonesia
  • Achmad Basuki Politeknik Elektronika Negeri Surabaya, Indonesia
Keywords: Support-Vector-Machine (SVM), Victim Detection, Tsunami Disaster Sites, Aerial Imaging, Histogram-of-Oriented-Gradients (HOG).


Finding victims at a disaster site is the primary goal of Search-and-Rescue (SAR) operations. Many technologies created from research for searching disaster victims through aerial imaging. but, most of them are difficult to detect victims at tsunami disaster sites with victims and backgrounds which are look similar. This research collects post-tsunami aerial imaging data from the internet to builds dataset and model for detecting tsunami disaster victims. Datasets are built based on distance differences from features every sample using Histogram-of-Oriented-Gradient (HOG) method. We use the longest distance to collect samples from photo to generate victim and non-victim samples. We claim steps to collect samples by measuring HOG feature distance from all samples. the longest distance between samples will take as a candidate to build the dataset, then classify victim (positives) and non-victim (negatives) samples manually. The dataset of tsunami disaster victims was re-analyzed using cross-validation Leave-One-Out (LOO) with Support-Vector-Machine (SVM) method. The experimental results show the performance of two test photos with 61.70% precision, 77.60% accuracy, 74.36% recall and f-measure 67.44% to distinguish victim (positives) and non-victim (negatives).


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R. Bethany, “Ring of Fire.†pp. 1–403, 1998.

BRR, “ACEH DAN NIAS SETAHUN SETELAH TSUNAMI Upaya Pemulihan Dan Langkah Ke Depan,†2005.

I. A. Sulistijono and A. Risnumawan, “From Concrete to Abstract : Multilayer Neural Networks for Disaster Victims Detection.â€

N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,†Proc. - 2005 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition, CVPR 2005, vol. I, pp. 886–893, 2005.

T. Kobayashi, A. Hidaka, and T. Kurita, “Selection of histograms of oriented gradients features for pedestrian detection,†Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4985 LNCS, no. PART 2, pp. 598–607, 2008.

Y. Uzun, M. Balcilar, K. Mahmoodi, F. Davletov, M. F. Amasyali, and S. Yavuz, “Usage of HoG (histograms of oriented gradients) features for victim detection at disaster areas,†ELECO 2013 - 8th Int. Conf. Electr. Electron. Eng., pp. 4–7, 2013.

C. Papageorgiou, “A Trainable System for Object Detection in Images and Video Sequences,†Int. J. Comput. Vis., vol. 38, no. 1685, pp. 15–33, 2000.

M. Andriluka et al., “Vision based victim detection from unmanned aerial vehicles,†IEEE/RSJ 2010 Int. Conf. Intell. Robot. Syst. IROS 2010 - Conf. Proc., pp. 1740–1747, 2010.

V. Ferrari, M. Marin-Jimenez, and A. Zisserman, “Progressive Search Space Reduction for Human Pose Estimation,†IEEE Conf. Comput. Vis. Pattern Recognit., vol. 2, no. figure 1, pp. 1–8, 2008.

L. Bourdev, J. Malik, U. C. Berkeley, A. Systems, P. Ave, and S. Jose, “Poselets : Body Part Detectors Trained Using 3D Human Pose Annotations,†2009 IEEE 12th Int. Conf. Comput. Vis., pp. 1365--1372, 2009.

P. Felzenszwalb, D. McAllester, and D. Ramanan, “A Discriminatively Trained, Multiscaled, Deformable Part Model,†Cvpr, pp. 1–8, 2008.

S. Carpin, M. Lewis, J. Wang, S. Balakirsky, and C. Scrapper, “USARSim: A robot simulator for research and education,†Proc. - IEEE Int. Conf. Robot. Autom., pp. 1400–1405, 2007.

W. R. Schwartz, A. Kembhavi, D. Harwood, and L. S. Davis, “Human detection using partial least squares analysis,†Comput. Vision, 2009 IEEE 12th Int. Conf., p. 1, 2009.

A. Bar-hillel, D. Levi, E. Krupka, and C. Goldberg, “LNCS 6314 - Part-Based Feature Synthesis for Human Detection,†Eur. Conf., pp. 127–142, 2010.

P. Dollár, Z. Tu, H. Tao, and S. Belongie, “Feature mining for image classification,†Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., no. July, 2007.

P. Dollár, Z. Tu, P. Perona, and S. Belongie, “Integral Channel Features,†BMVC 2009 London Engl., pp. 1–11, 2009.

P. Dollar, S. Belongie, and P. Perona, “The Fastest Pedestrian Detector in the West,†Proceedings Br. Mach. Vis. Conf. 2010, p. 68.1-68.11, 2010.

S. Walk and N. Majer, “[Poster]New Features and Insights for Pedestrian Detection,†Training, pp. 8–8.

C. Wojek and B. Schiele, “A performance evaluation of single and multi-feature people detection,†Jt. Pattern Recognit. Symp., pp. 82–91, 2008.

A. Satpathy, X. Jiang, and H. L. Eng, “Human detection by quadratic classification on subspace of extended histogram of gradients,†IEEE Trans. Image Process., vol. 23, no. 1, pp. 287–297, 2014.

P. Dollár, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 743–761, 2012.

S. Munder and D. M. Gavrila, “An experimental study on pedestrian classification,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 11, pp. 1863–1868, 2006.

D. S. Bolme, Y. M. Lui, B. a. Draper, and J. R. Beveridge, “Simple real-time human detection using a single correlation filter,†2009 Twelfth IEEE Int. Work. Perform. Eval. Track. Surveill., pp. 1–8, 2009.

D. S. Bolme, B. A. Draper, and J. R. Beveridge, “Average of synthetic exact filters,†2009 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. CVPR Work. 2009, pp. 2105–2112, 2009.

S. Soo, “Object detection using Haar-cascade Classifier,†vol. 2, no. 3, pp. 1–12, 2014.

How to Cite
Salfikar, I., Sulistijono, I. A., & Basuki, A. (2018). Automatic Samples Selection Using Histogram of Oriented Gradients (HOG) Feature Distance. EMITTER International Journal of Engineering Technology, 5(2), 234-254.