Ahmad Sani, Mohd Daud Kasmuni, Mahardhika Candra Prasetyahadi, Mohd Shafry Mohd Rahim and Mohd Shahrizal Sunar
UTM-IRDA Digital Media Centre
Faculty of Computing
Universiti Teknologi Malaysia,
81310 Skudai, Johor-Malaysia
ABSTRACT. We investigate the possibility for using portions of Histograms of Oriented Gradients (HOG) descriptors in a part- based people detection framework. Instead of extracting descriptors from isolated or pre-cropped human parts, we slice the extracted HOG descriptor from whole windows into four, one slice per one human part. Support Vector Machines (SVMs) are used for classifying the slices and the outcome detections are handled by a finite-state machine where three detected parts means that one assumed person is in the window being scanned. Experiments were conducted for our detection framework and another conventional one that uses whole HOG descriptors using images from the INRIA Person Dataset, in which our framework achieved better; detecting 46/50 of occluded people comparing to 36/50 for the conventional framework. Moreover, we achieved less false positive detections of 80 windows comparing to 289 for the conventional framework.
KEYWORDS. People detection; object detection; histograms of oriented gradients; partbased detection framework
REFERENCES
- Azizpour, H and Laptev, I, 2012 “Object Detection Using Strongly- Supervised Deformable Part Models,” in Computer Vision. vol. 7572, A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, and C.
- Schmid, Eds., ed Berlin: Springer Berlin Heidelberg, pp. 836-849.
- Dalal, N. and Triggs. B, 2005 “Histograms of oriented gradients for human detection,” in Conference on Computer Vision and Pattern Recognition San Diego, CA, pp. 886-893.
- Ahmad Sani, Mohd Daud Kasmuni, Mahardhika Candra Prasetyahadi, Mohd Shafry Mohd Rahim and Mohd Shahrizal Sunar Felzenszwalb, F., McAllester, D., and Ramanan, D., 2008 “A discriminatively trained,
multiscale, deformable part model,” in Computer Vision and Pattern Recognition, Anchorage, AKpp. 1 -8. - Linh, D., Buu, B., Vo, P. D., Tran, T. N. and Le, B. H., “Improved HOG Descriptors,” in 3rd International Conference on Knowledge and Systems Engineering, Hanoi, 2011, pp. 186-189.
- Marin, J., Vazquez, D., Lopez, A. M., Amores, J and Kuncheva, L. I., 2014 “Occlusion Handling via Random Subspace Classifiers for Human Detection,” Transactions on Cybernetics, vol. 44, pp.342-354.
- Mikolajczyk, K., Schmid, C., and Zisserman, A., 2004 “Human Detection Based on a Probabilistic Assembly of Robust Part Detectors,” in Computer Vision – ECCV 2004. vol. 3021, T. Pajdla and
- J. Matas, Eds., ed Berlin: Springer, pp. 69-82.
- Mikolajczyk, K., and Schmid, C., 2005 “A performance evaluation of local descriptors,” Pattern Analysis and Machine Intelligence, vol. 27, pp. 1615-1630.
- Mohan, A., Papageorgiou, C., and Poggio, T., 2001 “Example-based object detection in images by components,” Transactions on Pattern Analysis and Machine Intelligence, vol. 23, pp. 349-361.
- Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., and Poggio, T., 1997 “Pedestrian detection using wavelet templates,” in Conference on Computer Vision and Pattern Recognition, San Juan, pp. 193-199.
- Tang, S., Andriluka, M., and Schiele, B., 2013 “Detection and Tracking of Occluded People,” International Journal of Computer Vision, vol. 11263, pp. 1-12.
- Viola, P. and Jones, m., 2001 “Rapid object detection using a boosted cascade of simple features,” in Conference on Computer Vision and Pattern Recognition, Kauai, pp. 511 -518.
- Wang, X., Han, T. X., and Yan, S., 2009 “An HOG-LBP human detector with partial occlusion handling,” in 12th International Conference on Computer Vision, Kyoto, pp. 32-39.