A Personnel Detection Algorithm for an Intermodal Maritime Application of ITS Technology for Security at Port Facilities
Mouhammad K. Al Akkoumi, Robert C. Huck, James J. Sluss
DOI: 10.4236/jtts.2011.14016   PDF    HTML     4,897 Downloads   8,233 Views  


With an overwhelming number of containers entering the United States on a daily basis, ports of entry are causing major concerns for homeland security. The disruption to commerce to inspect all containers would be prohibitive. Currently, fences and port security patrols protect these container storage yards. To improve security system performance, the authors propose a low cost fully distributed Intelligent Transportation System based implementation. Based on prior work accomplished in the design and fielding of a similar system in the United States, current technologies can be assembled, mixed and matched, and scaled to provide a comprehensive security system. We also propose the incorporation of a human detection algorithm to enhance standard security measures. The human detector is based on the histogram of oriented gradients detec- tion approach and the Haar-like feature detection approach. According to the conducted experimental results, merging the two detectors, results in a human detector with a high detection rate and lower false posi- tive rate. This system allows authorized operators on any console to control any device within the facility and monitor restricted areas at any given time.

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Akkoumi, M. , Huck, R. and Sluss, J. (2011) A Personnel Detection Algorithm for an Intermodal Maritime Application of ITS Technology for Security at Port Facilities. Journal of Transportation Technologies, 1, 123-131. doi: 10.4236/jtts.2011.14016.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] “The national strategy for maritime security,” 2010. http://www.dhs.gov/xlibrary/assets/HSPD13_MaritimeSecurityStrategy.pdf
[2] R. H. Brakman and J. J. Limarzi, “ITS at the Hudson Valley Transportation Management Center,” IEEE Intelligent Systems, Vol. 19, No. 3, 2004, pp. 8-12. doi:10.1109/MIS.2004.13
[3] M. J. Kelly, D. J. Folds and N. Sobhi, “ATMS 2000: Hybrid Automation or a Lights Out Traffic Management Center?” Proceedings National Telesystems Conference, 1993, pp. 37-42. doi:10.1109/NTC.1993.293012
[4] R. Huck, J. Havlicek, J. Sluss, Jr. and A. Stevenson, “A Low-Cost Distributed Control Architecture for Intelligent Transportation Systems Deployment in the State of Okla- homa,” Proceedings IEEE International Conference Intelligent Transportation Systems, Vienna, Austria, 2005, pp. 919-924.
[5] N. Dalal, and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, 2005, pp. 886-893.
[6] P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, 2001, pp. I-511-I-518.
[7] L. Yun and Z. Peng, “An Automatic Hand Gesture Recognition System Based on Viola-Jones Method and SVMs,” IEEE International Workshop on Computer Science and Engineering, Vol. 2, 2009, pp. 72-76. doi:10.1109/WCSE.2009.769
[8] T. Ephraim, T. Himmelman and K. Siddiqi, “Real-Time Viola-Jones Face Detection in a Web Browser,” IEEE Canadian Conference on Computer and Robot Vision, 2009, pp. 321-328. doi:10.1109/CRV.2009.48
[9] D. Hefenbrock, J. Oberg, N. T. N. Thanh and R. Kastner, “Accelerating Viola-Jones Face Detection to FPGA- Level Using GPUs,” IEEE International Symposium on Field-Programming Custom Computing Machines, 2010, pp. 11-18. doi:10.1109/CRV.2009.48
[10] M. Kolsch and M. Turk, “Analysis of Rotational Robustness of Hand Detection with a Viola-Jones Detector,” Proceedings International Conference on Pattern Recognition, Santa Barbara, Vol. 3, 2004, pp. 107-110.
[11] T. Mita, T. Kaneko and O. Hori, “Joint Haar-like Features for Face Detection,” IEEE International Conference on Computer Vision, Beijing, Vol. 2, 2005, pp. 1619-1626.
[12] N. Seo, “Tutorial: OpenCV Haartraining,” 2008. http://note.sonots.com/SciSoftware/haartraining.html
[13] N. Dalal, “INRIA Person Dataset,” http://pascal.inrialpes.fr/data/human/
[14] D. Lowe, “Object Recognition from Local Scale-In- variant Features,” IEEE International Conference on Computer Vision, Vol. 2, 1999, pp. 1150-1157. doi:10.1109/ICCV.1999.790410
[15] H. X. Jia and Y. J. Zhang, “Fast Human Detection by Boosting Histograms of Oriented Gradients,” IEEE International Conference on Image and Graphics, 2007, pp. 683-688.
[16] Q. Zhu, M. C. Yeh, K. T. Cheng and S. Avidan, “Fast Human Detection Using a Cascade of Histograms of Oriented Gradients,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, 2006, pp. 1491-1498.
[17] C. Zhou, L. Tang, S. Wang and X. Ding, “Human Detection Based on Fusion of Histograms of Oriented Gradients and Main Partial Features,” International Congress on Image and Signal Processing, 2009, pp. 1-5. doi:10.1109/CISP.2009.5304536
[18] F. Porikli, “Integral Histogram: A Fast Way to Extract Histograms in Cartesian Spaces,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, 2005, pp. 829-836.
[19] H. Li and J. Cao, “Detection and Segmentation of Moving Objects Based on Support Vector Machine,” IEEE International Symposium on Information Processing, 2010, pp. 193-197.
[20] E. Pasolli, F. Melgani, M. Donelli, R. Attoui and M. De Vos, “Automatic Detection and Classification of Buried Objects in GPR Images Using Genetic Algorithms and Support Vector Machines,” IEEE Geoscience and Remote Sensing Symposium, Vol. 2, 2008, pp. II-525-II528.
[21] G. Zhu, C. Xu, Q. Huang and W. Gao, “Automatic Multi- Player Detection and Tracking in Broadcast Sports Video using Support Vector Machine and Particle Filter,” IEEE International Conference on Multimedia and Expo, 2006, pp. 1629-1632. doi:10.1109/ICME.2006.262859

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