Advanced image-processing tools for counting people in tourist site-monitoring applications

This work aims at demonstrating the usefulness of exploiting novel image-processing tools for moving-object detection and classification in the context of an actual application involving the remote monitoring of a tourist site. The application concerns outdoor people counting for tourist-flow estimation in a constrained environment. The technical problems to be solved are concerned with: (a) the design and implementation of low-complexity background updating and change detection algorithms able to adapt themselves to the time-varying illumination scene conditions, and (b) the integration of real-time pattern-recognition tools in order to distinguish group of persons to be counted from other objects present in the scene. The achieved results have proven that the proposed system makes it possible to obtain reliable people counting in different environmental situations, with an absolute mean error at most equal to 10%.

[1]  Fa-Long Luo,et al.  Applied neural networks for signal processing , 1997 .

[2]  Hans-Hellmut Nagel,et al.  New likelihood test methods for change detection in image sequences , 1984, Comput. Vis. Graph. Image Process..

[3]  Carlo S. Regazzoni,et al.  Distributed data fusion for real-time crowding estimation , 1996, Signal Process..

[4]  Alessandra Tesei,et al.  DEKF system for crowding estimation by a multiple-model approach , 1994 .

[5]  Carlo S. Regazzoni,et al.  “Long-Memory” Matching of Interacting Complex Objects from Real Image Sequences , 1997 .

[6]  Carlo S. Regazzoni,et al.  Remote cable-based video surveillance applications: the AVS-RIO project , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[7]  E. R. Davies.  Machine vision , 1990 .

[8]  Carlo S. Regazzoni,et al.  Advanced Video-Based Surveillance Systems , 1998 .

[9]  R.P.C. Wolters.  Characteristics of upstream channel noise in CATV-networks , 1996 .

[10]  Robert B. McGhee,et al.  Aircraft Identification by Moment Invariants , 1977, IEEE Transactions on Computers.

[11]  Jean Paul Frédéric Serra.  Morphological filtering: An overview , 1994, Signal Process..

[12]  Shyang Chang,et al.  Statistical change detection with moments under time-varying illumination , 1998, IEEE Trans. Image Process..

[13]  Aleksej Makarov.  Comparison of background extraction based intrusion detection algorithms , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[14]  M. Rossi,et al.  Tracking and counting moving people , 1994, Proceedings of 1st International Conference on Image Processing.

[15]  Franco Bartolini,et al.  Image sequence analysis for counting in real time people getting in and out of a bus , 1994, Signal Process..

[16]  Opas Chutatape,et al.  A modified Hough transform for line detection and its performance , 1999, Pattern Recognit..

[17]  Carlo S. Regazzoni,et al.  Use of Advanced Video Surveillance and Communication Technologies for Remote Monitoring of Protected Sites , 1999 .

[18]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[19]  Emrullah Durucan,et al.  Change detection with automatic reference frame update and key frame detector , 1999, NSIP.

[20]  Lucia Ballerini,et al.  Time-Varying Image Processing and Moving Object Recognition , 1997 .

[21]  Ramin Zabih,et al.  Counting people from multiple cameras , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.

[22]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[23]  Kazuhiko Hashimoto,et al.  People-counting system using multisensing application , 1998 .

[24]  T. J. Stonham,et al.  A system for counting people in video images using neural networks to identify the background scene , 1996, Pattern Recognit..

[25]  James R. Palmer.  CATV Systems-Design Philosophy and Performance Criteria as the Basis for Specifying Equipment Components , 1967 .

[26]  Ramesh C. Jain,et al.  Illumination independent change detection for real world image sequences , 1989, Comput. Vis. Graph. Image Process..

[27]  Carlo S. Regazzoni,et al.  A change-detection method for multiple object localization in real scenes , 1994, Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics.