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Vision-based Vehicle Detection in Intercity Roads for Intelligent Transportation Systems Applications
, M.Sc. Thesis Sharif University of Technology ; Marvasti, Farokh (Supervisor)
Abstract
This project aims to highlight vision related tasks centered around "car". First, we gathered a dataset of 4343 front view car images, captured from the streets of Iran and Syria during daylight, the images of which are all manually cropped around their corresponding accurately chosen bounding boxes. we also extracted seven parts (i.e. left and right front lights, left and right mirrors, bumper, plate, and air intake) from each car image in the dataset. Our dataset is suitable for developing and testing bounding box extraction algorithms, holistic and part based analyses, occlusion handling algorithms, etc. next, we utilized Viola-Jones Detector to develop a system for car detection, in...
Efficient feature extraction for highway traffic density classification
, Article 9th Iranian Conference on Machine Vision and Image Processing, 18 November 2015 through 19 November 2015 ; Volume 2016-February , 2015 , Pages 14-19 ; 21666776 (ISSN) ; 9781467385398 (ISBN) ; Ahmadi, P ; Gholampour, I ; Sharif University of Technology
IEEE Computer Society
Abstract
Traffic density estimation is one of the most challenging problems in Intelligent Transportation Systems. In this paper, we estimate the traffic flow density based on classification. Various new efficient features are introduced for distinguishing between different traffic states, including number of key-points, edges of difference-image and moving edges. These features describe the traffic flow without any need to individual vehicles detection and tracking. We experiment our proposed approach on a standard database and some real videos from Tehran roads. The results show high accuracy performance of our method, even in changes of environmental conditions (e.g., lighting), by using efficient...
A new method for traffic density estimation based on topic model
, Article Signal Processing and Intelligent Systems Conference, 16 December 2015 through 17 December 2015 ; 2015 , Pages 114-118 ; 9781509001392 (ISBN) ; Ahmadi, P ; Gholampour, I ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
Abstract
Traffic density estimation plays an integral role in intelligent transportation systems (ITS), using which provides important information for signal control and effective traffic management. In this paper, we present a new framework for traffic density estimation based on topic model, which is an unsupervised model. This framework uses a set of visual features without any need to individual vehicle detection and tracking, and discovers the motion patterns automatically in traffic scenes by using topic model. Then, likelihood value allocated to each video clip enables us to estimate its traffic density. Results on a standard dataset show high classification performance of our proposed...