Evaluation of statistical and multiple-hypothesis tracking for video traffic surveillance

Authors:
Jeffrey E. Boyd;Jean Meloche
Affiliations:
Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada;Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Venue:
Machine Vision and Applications
Year:
2003

Citing 6
Cited 1

Tracking and data association

Tracking and data association
Numerical recipes in C (2nd ed.): the art of scientific computing

Numerical recipes in C (2nd ed.): the art of scientific computing
An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Using Adaptive Tracking to Classify and Monitor Activities in a Site

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Real-Time High Density People Counter Using Morphological Tools

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4

Sequential coordinate-wise algorithm for the non-negative least squares problem

CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns

Quantified Score

Hi-index	0.00

Visualization

Abstract

Conventional tracking methods encounter difficulties as the number of objects, clutter, and sensors increase, because of the requirement for data association. Statistical tracking, based on the concept of network tomography, is an alternative that avoids data association. It estimates the number of trips made from one region to another in a scene based on interregion boundary traffic counts accumulated over time. It is not necessary to track an object through a scene to determine when an object crosses a boundary. This paper describes statistical tracing and presents an evaluation based on the estimation of pedestrian and vehicular traffic intensities at an intersection over a period of 1 month. We compare the results with those from a multiple-hypothesis tracker and manually counted ground-truth estimates.