Audio–Video based People Counting and Security Framework for Traffic Crossings
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In this paper, we propose a traffic management system which uses both audio and video data at intersections to prevent traffic congestion. A novel scheme of counting and tracking crowd with a single overhead camera is proposed for the purpose of real-time traffic control at crossings as well as for adjusting the carrier frequency using the video data. The system recognizes people movement along various directions estimating the possibility of traffic congestion. To carry out the tracking procedure, various temporal and spatial features of the images are used to identify the people in the crowd in order to predict the position of the objects in the current frame. Several issues such as emergence of people, departure of people, occlusions, and de-occlusions are resolved through interactions between regions and objects. With the help of audio data at counting region, we identify children by their voice using pitch analysis. The database constitutes of voices of speakers of all ages and of both genders. The paper also includes automated accident detection at the traffic intersections through use of audio data at intersections. It can classify the sounds into “crash”, “power brake” and “normal traffic” sounds. The experimental results show the effectiveness of our framework.
- J. Segen, and S. Pingali, “A Camera-Based System for Tracking People in Real Time,” IEEE Proc. Int. Conf. Pattern Recognit., no. 3, 1996, pp. 63–67.
- Wren, C., Azarbayejani, A., Darrell, T., Pentland, A. (1997) Pfinder: Real-Time Tracking of the Human Body. IEEE Trans. Pattern Anal. Mach. Intell. 19: pp. 780-785 CrossRef
- I. Haritaoglu, D. Harwood, and L. Davis, “W4-Real Time Detection and Tracking of People in 2.5D,” European Conf. Comput. Vision, Germany, 1998.
- R. Rosales, and S. Sclaroff, “3D Trajectory Recovery for Tracking Multiple Objects and Trajectory Guided Recognition of Actions,” IEEE Comput. Soc. Conf. CVPR, vol. 2, June 1999, pp. 23–25.
- T. Horprasert, D. Harwood, and L. Davis, “A Statistical Approach for Real-Time Robust Background Subtraction and Shadow Detection,” Proc. IEEE ICCV Frame-rate Workshop, 1999.
- Stauffer, C., Grimson, W.E.L. (2000) Learning Patterns of Activity Using Real-Time Tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22: pp. 747-757 CrossRef
- K. Rohr, “Towards Model-Based Recognition of Human Movement in Image Sequences,” CVGIP: Image Understanding, vol. 59, no. 1, Jan. 1994.
- O. Masoud, and N.P. Papanikolopoulos, “A Novel Method for Tracking and Counting Pedestrians in Real-Time Using a Single Camera,” IEEE Trans. Veh. Technol., no. 50, 2001, pp. 1267–1278.
- M. Rossi, and A. Bozzoli, “Tracking and Counting Moving People,” IEEE Proc. Int. Conf. Image Process., no. 3, 1994, pp. 212–216.
- K. Terada, D. Yoshida, S. Oe, and J. Yamaguchi, “A Counting Method of the Number of Passing People Using a Stereo Camera,” IEEE Proc. Industrial Electronics Conf., no. 3, 1999, pp. 1318–1323.
- S.A. Niyogi and E.H. Adelson, “Analyzing and Recognizing Walking Figures in XYT,” IEEE Conf. Comput. Vision Pattern Recognit., July 1997, pp. 469–474.
- R. Cutler and L. Davis, “Real-Time Periodic Motion Detection, Analysis and Applications,” Proc. IEEE Conf. Comput. Pattern Recognit., Fort Collins, USA, 1999, pp. 326–331.
- R. Polana and R. Nelson, “Low Level Recognition of Human Motion,” IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin, 1994, pp. 77–82.
- H.J. Payne, E.D. Helfenbein, and H.C. Knobel, “Development and Testing of Incident Detection Algorithms: Volume 2: Research Methodology and Detailed Results,” Report No. FHWA-RD-76-20, Washington, D.C., Federal Highway Administration, 1976.
- J.M. McDermott, “Incident Surveillance and Control on Chicago-Area Freeways,” Special Report 153: Better Use of Existing Transportation Facilities. TRB, National Research Council, Washington, D.C., 1975, pp. 123–140.
- Whitney, D.A., Pisano, J.J. (1995) AutoAlert: Automated Acoustic Detection of Incidents. TASC, Inc., Reading, Massachusetts
- K.W. Dickinson and C.L. Wan, “An Evaluation of Microwave Vehicle Detection at Traffic Signal Controlled Intersection,” Proc. Third Int. Conf. Road Traffic Control, 1990, pp. 153–157.
- Otsu, N (1979) A Threshold Selection Method From Gray-Level Histograms. IEEE Trans. Syst. Man Cybern. 9: pp. 62-66 CrossRef
- O.D. Trier and T. Taxt, “Evaluation of Binarization Methods for Document Images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 3, Mar. 1995.
- Niblack, W. (1986) An Introduction to Digital Image Processing. Prentice Hall, Englewood Cliffs, N.J.
- Y. Keller, A. Averbuch, and O. Miller, “Robust Phase Correlation,” ICPR no. 2, 2004, pp. 740–743.
- W. S. Hoge, D. Mitsouras, F. J. Rybicki, R. V. Mulkern, and C.F. Westin. “Registration of Multi-dimensional Image Data Via Sub-pixel Resolution Phase Correlation,” Proc. IEEE Intl. Conf. Image Process (ICIP-03), vol. II, Barcelona, Spain, Sep. 2003, pp. 707–710.
- Toivonen, T., Heikkila, J. (2003) Efficient Method for Half-Pixel Block Motion Estimation Using Block Differentials, International Workshop VLBV. Springer, Madrid, Spain
- L.R. Rabiner, M.J. Cheng, A.E. Rosenberg, and C.A. Mcgonegal, “A Comparative Performance Study of Several Pitch Detection Algorithms,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-24, no. 5, October 1976.
- Y Zhang, R. Hu, L.M. Bruce, N. Balraj and S. Yu, “Development of Real-Time Automated Accident Detection System at Intersections,” Annual Conference of the Transportation Research Board, Washington D.C., 2004.
- Mallat, S.G. (1989) A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. Pattern Anal. Mach. Intell. II: pp. 674-693 CrossRef
- Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition. Academic, San Diego, California
- R. Duda, P. Hart, and D. Stork, “Pattern Classification”, Wiley, 2001.
- Audio–Video based People Counting and Security Framework for Traffic Crossings
The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology
Volume 49, Issue 3 , pp 377-391
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
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- accident detection
- crowd detection
- image processing
- people counting
- phase correlation
- pitch detection
- road traffic control
- Industry Sectors