Moving Vehicle Detection and Information Extraction Based on Deep Neural Network
Published in 2017 Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV); Athens, (2017)., 2017
Recommended citation: Ihsan Ullah et al. (2017). Moving Vehicle Detection and Information Extraction Based on Deep Neural Network; 2017 Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV); Athens, (2017).. 1(1). https://ihsan149.github.io/files/c3.pdf
In recent years, vehicle recognition has become an important application in intelligent traffic monitoring and management. Vehicle analysis is an essential component in many intelligent applications, such as automatic toll collection, driver assistance systems, self-guided vehicles, intelligent parking systems, and traffic statistics (vehicle count, speed, and flow). The main goal of our study is to extract the information from the moving vehicles like their make, model and type. We address the vehicle detection and recognition problems using Deep Neural Networks (DNNs) approach. Our proposed approach outperforms state-of-the-art method. We first detect the moving vehicle based on frame difference and then extract the frontal part of the vehicle based on symmetrical filter, the frontal part of the vehicle is fed into the deep architecture for recognition. The Top 1 accuracy of proposed VMMTR algorithm is 96.31%.Our method achieves promising results on image.
#Proposed Framework and Examples #