Holistic Recognition of Low Quality License Plates by CNN using Track Annotated Data [IWT4S-AVSS 2017]Abstract: This work is focused on recognition of license plates in low resolution and low quality images. We present a methodology for collection of real world (non-synthetic) dataset of low quality license plate images with ground truth transcriptions. Our approach to the license plate recognition is based on a Convolutional Neural Network which holistically processes the whole image, avoiding segmentation of the license plate characters. Evaluation results on multiple datasets show that our method significantly outperforms other free and commercial solutions to license plate recognition on the low quality data. To enable further research of low quality license plate recognition, we make the datasets publicly available.
3rd Best Paper Award
Abstract: This paper presents a fully automated system for traffic surveillance which is able to count passing cars, determine their direction, and the lane which they are taking. The system works without any manual input whatsoever and it is able to automatically calibrate the camera by detecting vanishing points in the video sequence. The proposed system is able to work in real time and therefore it is ready for deployment in real traffic surveillance applications. The system uses motion detection and tracking with the Kalman filter. The lane detection is based on clustering of trajectories of vehicles. The main contribution is a set of filters which a track has to pass in order to be treated as a vehicle and the full automation of the system.
Abstract: A system for traffic analysis was designed and implemented during work on this thesis. The system is able to detect, track and classify vehicles. Also, the system is able to detect lanes or determine whether a vehicle is passing in wrong way. The speed of observed vehicles is also measured. The system does not require any manual input or calibration whatsoever as the video camera is fully automatically calibrated by detected vanishing points. The accuracy of the detection, tracking and classification is high and the speed of vehicles is measured with a low error. The system runs in real time and it is currently used for a continuous monitoring of traffic. The main contribution of the thesis is the fully automated speed measurement of passing vehicles.