Traffic Volume Study Using Automated Surveillance Technique

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Traffic Volume Study Using Automated Surveillance Technique

Traffic Volume Study Using Automated Surveillance Technique

Abstract

Traffic Volume Study Using Automated Surveillance Technique paper presents a novel algorithm for advance Traffic Surveillance by vehicle counting and classification, based on the image processing theory. Vehicle counting is done by Background subtraction and finding the centroid. Classification is done by thresholding method. A reference frame is initially used and considered as background information. While a new object enters into the frame, is detected by background subtraction.

The foreground information and background information are identified using the reference frame as background model. Video sequences have been captured and tested with the proposed algorithm. Experimental results, which demonstrate the system’s performance, are also shown.

Traffic Volume Study Using Automated Surveillance Technique

Conclusion

Although the obtained results are promising, the algorithm still needs further modifications. To enable detecting and tracking in day and night recordings, background subtraction with shadow elimination techniques can be used. In order to improve results lane based tracking of vehicles can be implemented rather than tracking vehicles all over the highway. Cross-roads can be added as an extension to special considerations since it becomes challenging to track vehicles as they change directions. To differentiate the no-vehicle case from the congested traffic case, bounding-box sizes of detected vehicles will be used