Abstract: Intelligent transportation systems enables the analysis of large multidimensional street traffic data to detect pattern and anomaly, which otherwise is a difficult task. Advancement in computer vision makes great contribution in the progress of video based traffic surveillance system. But still there are some challenges which need to be solved like objects occlusion, behavior of objects. This paper developed a novel framework which explores multidimensional data of road traffic to analyze different patterns of traffic and anomaly detection. This framework is implemented on road traffic dataset collected from different areas of the city.

Muhammad Umer Farooq, Najeed Ahmed Khan and Mir Shabbar Ali. “Unsupervised Video Surveillance for Anomaly Detection of Street Traffic”. International Journal of Advanced Computer Science and Applications (IJACSA) 8.12 (2017). http://dx.doi.org/10.14569/IJACSA.2017.081234

@article{Farooq2017,
title = {Unsupervised Video Surveillance for Anomaly Detection of Street Traffic},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.081234},
url = {http://dx.doi.org/10.14569/IJACSA.2017.081234},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {12},
author = {Muhammad Umer Farooq and Najeed Ahmed Khan and Mir Shabbar Ali}
}