Estimating Pedestrian Accident Exposure: Automated Pedestrian Counting Devices Report

Automated methods are commonly used to count motorized vehicles, but are not frequently used to count pedestrians. This is because the automated technologies available to count pedestrians are not very developed, and their effectiveness has not been widely researched. Moreover, most automated methods are used primarily for the purpose of detecting, rather than counting, pedestrians (Dharmaraju et al., 2001; Noyce and Dharmaraju, 2002; Noyce et al., 2006). Automated pedestrian counting technologies are attractive because they have the potential to reduce the labor costs associated with manual methods, and to record pedestrian activity for long periods of time that are currently difficult to capture through traditional methods. Data input and storage may also be less time consuming than with manual methods. On the other hand, the capital costs of automated equipment may be high; specialized training may be required to operate it; and automated devices are generally not capable of collecting information on pedestrian characteristics and behavior. For these reasons, automated devices are not appropriate for all pedestrian data collection efforts. The choice between which method is more appropriate to collect pedestrian data must be based on the accuracy level desired, budget constraints, and data needs specifications. Automated Counting Technologies Much of the research on automated pedestrian tracking devices has focused on pedestrian detection, not pedestrian counting. Extensive reviews of pedestrian detection technologies were conducted by Noyce and Dharmaraju (2002) and by Chan et al. (2006). Technologies include piezoelectric sensors, acoustic, active and passive infrared, ultrasonic sensors, microwave radar, laser scanners, video imaging (computer vision). Of the technologies listed above, those most adaptable to the purpose of pedestrian counting are: infra-red beam counters; passive infrared counters; piezoelectric pads; laser scanners; and computer vision technology. None of these devices are widely used for the purpose of counting pedestrians outdoors, but all have some potential to be adapted for that purpose. This report describes each of these technologies in detail, and discusses some of the technical strengths and weaknesses of each method. It is important to be aware that technical limitations are only one consideration among many when choosing an appropriate counting device. The device “packaging,†such as the method and location of installation may be equally important. For example, the location and accessibility of the device may create liability issues or promote vandalism.

[1]  Hai Tao,et al.  Counting Pedestrians in Crowds Using Viewpoint Invariant Training , 2005, BMVC.

[2]  Osama Masoud,et al.  A novel method for tracking and counting pedestrians in real-time using a single camera , 2001, IEEE Trans. Veh. Technol..

[3]  Victoria Gitelman,et al.  AN EVALUATION OF CROSSWALK WARNING SYSTEMS , 2001 .

[4]  Louahdi Khoudour,et al.  PEDESTRIANS COUNTING IN PUBLIC TRANSPORT AS AN EXPLOITATION HELP FOR OPERATORS , 1998 .

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

[6]  K. Dietmayer,et al.  Object tracking and classification for multiple active safety and comfort applications using a multilayer laser scanner , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[7]  David A Noyce,et al.  Development of Bicycle and Pedestrian Detection and Classification Algorithm for Active-Infrared Overhead Vehicle Imaging Sensors , 2006 .

[8]  David Binnie,et al.  Monitoring the Movement of Pedestrians Using Low-cost Infrared Detectors: Initial Findings , 2004 .

[9]  K. Dietmayer,et al.  Multiple hypothesis classification with laser range finders , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[10]  Serge J. Belongie,et al.  Counting Crowded Moving Objects , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[11]  Peter H. Tu,et al.  Detecting and counting people in surveillance applications , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

[12]  Janne Heikkilä,et al.  A real-time system for monitoring of cyclists and pedestrians , 2004, Image Vis. Comput..

[13]  Sung-Jea Ko,et al.  Real-Time System for Counting the Number of Passing People Using a Single Camera , 2003, DAGM-Symposium.

[14]  Herman F Huang,et al.  AUTOMATED PEDESTRIAN DETECTION USED IN CONJUNCTION WITH STANDARD PEDESTRIAN PUSH BUTTONS AT SIGNALIZED INTERSECTIONS , 1999 .

[15]  D A Noyce,et al.  AN EVALUATION OF TECHNOLOGIES FOR AUTOMATED DETECTION AND CLASSIFICATION OF PEDESTRIANS AND BICYCLISTS , 2002 .

[16]  Tomaso A. Poggio,et al.  Trainable pedestrian detection , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[17]  R. Allsopp,et al.  IMAGE PROCESSING FOR THE ANALYSIS OF PEDESTRIAN BEHAVIOUR , 1997 .

[18]  A. Dupret,et al.  On chip vision system architecture using a CMOS retina , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[19]  C. Thorpe,et al.  Dressed human modeling, detection, and parts localization , 2001 .

[20]  J. Scholz,et al.  Reliable pedestrian protection using laserscanners , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[21]  B. Zavidovique,et al.  A context-dependent vision system for pedestrian detection , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[22]  Ryosuke Shibasaki,et al.  A novel system for tracking pedestrians using multiple single-row laser-range scanners , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[23]  Jon M. Kerridge,et al.  Using Low-Cost Infrared Detectors to Monitor Movement of Pedestrians: Initial Findings , 2004 .

[24]  Jean-Philippe Thiran,et al.  Counting Pedestrians in Video Sequences Using Trajectory Clustering , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[25]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Hideo Mori,et al.  A method for discriminating of pedestrian based on rhythm , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

[27]  Herman F Huang,et al.  ITS AND PEDESTRIAN SAFETY AT SIGNALIZED INTERSECTIONS , 1999 .

[28]  A. Shashua,et al.  Pedestrian detection for driving assistance systems: single-frame classification and system level performance , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[29]  J. Casas,et al.  Mutual feedback scheme for face detection and tracking aimed at density estimation in demonstrations , 2005 .

[30]  Robert J. Schneider,et al.  Pedestrian and bicycle data collection in United States communities: quantifying use, surveying users, and documenting facility extent , 2005 .

[31]  Ching-Yao Chan,et al.  Experimental Vehicle Platform for Pedestrian Detection , 2006 .