Department Publications

An informative digital map is a prerequisite for future Intelligent Transportation System (ITS) applications such as lane level navigation systems. Preparing and updating such detailed digital maps by using existing methods such as surveying and image digitization is not practical due to the time and cost involved. We propose a method that statistically mines Global Positioning System (GPS) trajectory data obtained from vehicles to generate a lane level digital map of the road. The proposed method is capable of generating a digital map of the road which contains lane centerlines. The proposed method is independent of the lane width, lane parallelism and can handle lane splits and merges. In addition, a method that can be used to generate a map of lane boundaries is also presented. The proposed methods have been tested using the GPS data collected using vehicles equipped with GPS enabled mobile phones …

Typical Automatic Number Plate Recognition (ANPR) system uses high resolution cameras to acquire good quality images of the vehicles passing through. In these images, license plates are localized, characters are segmented, and recognized to determine the identity of the vehicles. However, the steps in this workflow will fail to produce expected results in low resolution images and in a less constrained environment. Thus in this work, several improvements are made to this ANPR workflow by incorporating intelligent heuristics, image processing techniques and domain knowledge to build an ANPR system that is capable of identifying vehicles even in low resolution video frames. Main advantages of our system are that it is able to operate in real-time, does not rely on special hardware, and not constrained by environmental conditions. Low quality surveillance video data acquired from a toll system is used to …

We present a novel method where smart phone sensors are used to sense the prevailing traffic situation and to share extracted information among a community. Instead of using the power-hungry GPS receiver in the mobile device for detecting congestion through vehicular motion, we use the accelerometer along with a novel algorithm. We show that traffic congestion situations can be identified with 78% accuracy with our technique. Vehicular traffic has become a severe problem, particularly in the less developed countries. Therefore pre awareness of congestion situations is very important for an efficient lifestyle.