Surveillance of Pedestrian Bridge Traffic using Neural Networks


A computer-vision monitoring system is demonstrated that automatically detects the presence and location of people. The approach investigated the potential for real-time, automated surveillance and tracking in a realistic environment. Economy was obtained by the use of gray-scale, fixed perspective images and efficiency was obtained by the use of selected object features and a neural-network-processing algorithm. The system was applied to pedestrian traffic on an outdoor bridge and consequently had to handle complex images. Image sequences of single and multiple people were used with differences in clothing, position, lighting, season, etc. A two-stage algorithm was implemented in which (1) new objects were identified in a highly variable scene and (2) the objects were classified with a back-propagation neural network. The image processing techniques included segmentation and filtering and the neural network used fourteen object features as inputs. The implementation had excellent people-discrimination accuracy despite the noise in the images and had low computational complexity with respect to alternative techniques.

Meeting Name

SPIE 7292, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems (2009: Mar. 8, San Diego, CA)


Electrical and Computer Engineering


The Society of Photo-Optical Instrumentation Engineers (SPIE); American Society of Mechanical Engineers; SISTeC; Korea Advanced Institute of Science and Technology

Keywords and Phrases

Pedestrians; Security; Smart Structures; Surveillance; Tracking; Backpropagation Algorithms; Computational Complexity; Concrete Bridges; Footbridges; Image Segmentation; Imaging Techniques; Monitoring; Sensors; Neural Networks

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2009 SPIE, All rights reserved.

Publication Date

01 Mar 2009