| Paper: | ITT-L2.6 | ||
| Session: | Defense Applications | ||
| Time: | Friday, May 21, 11:10 - 11:30 | ||
| Presentation: | Lecture | ||
| Topic: | Industry Technology Track: Automotive Applications | ||
| Title: | FUSION OF WAVELET TRANSFORM AND COLOR INFORMATION FEATURES FOR AUTOMATIC VEHICLE REIDENTIFICATION IN INTELLIGENT TRANSPORTATION SYSTEMS | ||
| Authors: | Glenn Arr; Rowan University | ||
| Carlos Sun; University of Missouri-Columbia | |||
| Ravi Ramachandran; Rowan University | |||
| Abstract: | Vehicle reidentification is the process of reidentifying or tracking vehicles from one point on the roadway to the next.By performing vehicle reidentification, important traffic parameters including travel time, section density and partial dynamic origin/destinationdemands can be obtained.This provides for anonymous tracking of vehicles from site-to-site and has the potential for improving Intelligent Transportation Systems (ITS)by providing more accurate data.This paper presents a fusion based vehicle reidentification algorithm that uses four different features, namely, (1) the wavelet transform of the inductive signature vector acquired from loop detectors, (2) vehiclevelocity, (3) traversal time and (4) color information (based on images acquired from video cameras) to achieve high accuracy.A nearest neighbor approach classifies the features and linear feature fusion is shown to improve performance. With the fusion of four features, more than a 92 percent accuracy is obtained on real data collected from a parkway in California.Also, it is found that the wavelet transform improves performance and reduces the dimension of the feature vector when compared to the raw vehicle signatures. | ||
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