Abstract

Many academic sensornet systems consider the problem of tracking targets as they move through a fixed field of sensors. Such applications must relate sensor detections to specific targets, yet prior work has often ignored this problem, assuming either a single target, sufficient spatial separation that target-to-sensor mapping is clear, or some out-of-band detection-totarget mapping. There has been little study of algorithms to associate detections with targets, and effects of their accuracy on detection results, particularly in environments with dense targets. We explore the question of detection-to-target mapping in the context of a vehicle classification system for urban roadways, where vehicles pass fixed sensors at varying but frequent rates. We develop several signature matching algorithms that relate detections at different sensors to same or different vehicles. We evaluate these algorithms with data taken in a field test of live traffic compared against ground truth obtained through manual analysis of video and the resulting matching recall is over 78%. We investigate the effects of mapping accuracy on length-based vehicle classification. We show that accurate signature matching is critical to multi-sensor algorithms. We compare our matching algorithms against an oracle (perfect information), and find that all matching reduces end-to-end accuracy somewhat, but a poor

Date

2011

Authors

Chengjie Zhang, John Heidemann

Publisher

Technical Report ISI-TR-2011-675, USC/ISI