Fast calculation of histogram of oriented gradient feature by removing redundancy in overlapping block

Journal of Information Science and Engineering

Volumn 30, Issue 6, 2014, Pages 1719-1731

  Kim, Soojin ^a   Cho, Kyeongsoon ^a  

^a Hankuk University of Foreign Studies   (South Korea)

Author keywords

Fast detection time; High detection rate; Histogram of oriented gradient; Pedestrian detection; Trilinear interpolation

Indexed keywords

GRAPHIC METHODS; INTELLIGENT ROBOTS; INTERPOLATION; REDUNDANCY;

FAST DETECTIONS; HIGH DETECTION RATE; HISTOGRAM OF ORIENTED GRADIENTS; PEDESTRIAN DETECTION; TRILINEAR INTERPOLATION;

FEATURE EXTRACTION;

EID: 84911865728     PISSN: 10162364     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (14)

1. N. Dalal and B. Triggs, "Histogram of oriented gradients for human detection," in Proceedings of International Conference on Computer Vision and Pattern Recognition, 2005, pp. 886-893.
2. P. Dollar, C. Wojek, B. Schiele, and P. Perona, "Pedestrian detection: an evaluation of the state of the art," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, 2012, pp. 743-761.
3. M. Enzweiler and D. M. Gavrila, "Monocular pedestrian detection: survey and experiments," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, 2009, pp. 2179-2195.
4. C. Wojek and B. Schiele, "A performance evaluation of single and multi-feature people detection," in Proceedings of DAGM Symposium on Pattern Recognition, 2008, pp. 82-91.
5. T. Ojala, M. Pietikainen, and D. Harwood, "A comparative study of texture measures with classification based on feature distributions," Pattern Recognition, Vol. 29, 1996, pp. 51-59.
6. M. Farhadi, S. A. Motamedi, and S. Sharifian, "Efficient human detection based on parallel implementation of gradient and texture feature extraction methods," in Proceedings of the 7th Iranian Machine Vision and Image Processing, 2011, pp. 1-5.
7. P. Dollar, S. Belongie, and P. Perona, "The fastest pedestrian detector in the West," in Proceedings of British Conference on Machine Vision, 2010, pp. 1-11.
8. K. Negi, K. Dohi, Y. Shibata, and K. Oguri, "Deep pipelined one-chip FPGA implementation of a real-time image-based human detection algorithm," in Proceedings of International Conference on Field-Programmable Technology, 2011, pp. 1-8.
9. B. Bilgic, B. K. P. Horn, and I. Masaki, "Fast human detection with cascaded ensemble on the GPU," in Proceedings of IEEE Intelligent Vehicles Symposium, 2010, pp. 325-332.
10. K. Mizuno, Y. Terachi, K. Takagi, and S. Izumi, "Architectural study of HOG feature extraction processor for real-time object detection," in Proceedings of IEEE Workshop on Signal Processing Systems, 2012, pp. 197-202.
11. M. Enzweiler and D. M. Gavrila, "Monocular pedestrian detection: survey and experiment," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, 2009, pp. 2179-2195.
12. C. Papageorgiou and T. Poggio, "A trainable system for object detection," International Journal on Computer Vision, Vol. 38, 2000, pp. 15-33.
13. V. N. Vapnik, Statistical Learning Theory, John Wiley & Sons, NY, 1998.
14. Intel Open CV Library, http://www.opencv.org.