Vehicle Detection in Denser Environment Using Gaussian Model
DOI:
https://doi.org/10.26438/ijcse/v7i8.4448Keywords:
object detection, precision, occlusion rate, accuracy, false alarmAbstract
Vehicle area n/w (VANET) has been come a long distance since its inception. After smart cities and smart village, smart roads are required to manage the traffic effectively and efficiently. VANET recognize a vehicle and trace it. Establishing connection and serving the request come once a vehicle is recognized appropriately and trekked it serves a great help in video surveillance of moving objects too. Purpose of surveillance but recognizing them in a difficult environment is always a challenge the proposed work detects single moving vehicles and multiple moving vehicles under dense environment like foggy condition. The frames are read as images, noise is filtered on two Averaging and Median filter. An improvised Gaussian mixture model on two dimensional structural elements has been proposed in the thesis. The results obtained are compared with standard optical flow algorithm to detect moving vehicles; the proposed algorithm improves false alarm rate, precision, accuracy, occlusion rate. It concludes that the proposed algorithm works better than existing optical flow algorithm for single and multiple vehicle detection in a dense environment.
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