Vehicle Detection in Night Time using Background Model in Urban Traffic Environment
U Pavan Kumar1, Bharathi S H2
1U Pavan Kumar, Research Scholar, REVA University, School of Electronics and Communication Engineering, Bengaluru, India.
2Bharathi S H, Professor, REVA University, School of Electronics and Communication Engineering, Bengaluru, India.
Manuscript received on 01 June 2019 | Revised Manuscript received on 07 June 2019 | Manuscript published on 30 June 2019 | PP: 2532-2537 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6936068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In image processing the trendy work is to detect the vehicle in night time; the main aim is to detect the vehicle both in the daytime and the nighttime using the background subtraction model in the urban traffic environment. In this case the image will not be clear in the night time so there have developed an algorithm to resolve this issue and the algorithm used here is head light detection. By using this, we can make to known the night time vehicle is present or not. This is done by two step of detection there are of detect the head light that is bright pixel extraction and the other one is the headlight identification. And then the Saliency Map generation is used to find the foreground object detection in the image and it will provide the better result in the night time video sequences and finally by using the morphological filter they will remove the false positive in the pixel. Experimental result shows a better output than the previous method and the planned work will gives a enhanced result in the Accuracy, Recall and the Execution time when compare to the previous algorithm.
Index Terms: Background Subtraction, Head Light Detection, Bright Pixel Extraction, Head Light Identification, Saliency Map Generation, Morphological Filter.
Scope of the Article: Environmental Engineering