• Shyam Kumar
• Sparsh Sharma
• Mohit Pandey

Traffic analysis has been a problem that city planners have been planning and developing for decades. There has been a number of ways developed to analyzed and streamline the traffic issue. At a particular period of time in a certain path, the count of vehicles is what is analyzed. The systems that are developed over the period are consisting of a number of sensors like a radar gun, microwave sensors, pressure tubes, magnetic loop, GPS based traffic analysis, etc. Over time the systems so developed have matured and are showing far better functionality than before. But the issue that exists with them is the high cost of the hardware resources, the lifetime of the sensors, limited information, reliability in various light and weather conditions, etc. The issue with them is that these systems require maintenance and periodic calibration. Therefore, this study has involved a computer vision based vehicle detection and counting system. The system involves a camera unit that gives us a view of the traffic in locality and then the system extracts every single frame of the video and then applies the Haar-cascade and number of graphical and mathematical modules on it which separates the vehicles from the background and then increases the count respectively.

Vehicle detection, Vehicle recognition, Video surveillance, Video classification, Traffic Detection, Haar-Cascade.

"GRID LOCK – “TRAFFIC DETECTION USING PYTHON AND OPENCV”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.507-510, May-2019, Available :http://www.jetir.org/papers/JETIR1905F76.pdf

"GRID LOCK – “TRAFFIC DETECTION USING PYTHON AND OPENCV”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp507-510, May-2019, Available at : http://www.jetir.org/papers/JETIR1905F76.pdf