• Prof. Jondhale S.S.
• Shingote Vijaya R.
• Badhe Tanuja M.
• Inamdar Safiya A.
• Temgire Sonali B.

Almost all (metropolitan) cities including the major ones, like Los Angeles, Beijing, New York, are suffering from heavy traffic congestion. Statistics show that, in 2015, 43 cities in China are suffering a prolonged travel time of more than 1.5 h every day during rush hours. In the meanwhile, traffic accidents are plaguing the economic development as well. In order to achieve a better performance of detection and recognition of multi-vehicle targets in complex urban environment, a step of detection algorithm based on the features of Histogram of Oriented Gradients (HOG). This algorithm makes full use of HOG characteristic advantages for target vehicles, i.e., the good descriptive ability of HOG feature. With the ever-increasing demand in urban mobility and modern logistics sector, the vehicle population has been steadily growing over the past several decades. One natural consequence of the vehicle population growth is the increase in traffic congestion. In our proposed method, the system is designed to manage traffic signal timings based on the density of traffic on its corresponding road. Detecting traffic through roadside CCTV cameras. It acts as multi-class classification which is recognizing traffic. The system detect traffic event in real-time. We here propose a density based traffic signal scheduling algorithm.

Multi-class classification, Histogram of Oriented Gradients, Detection, Recognition, scheduling algorithm.

"Machine Learning Based Automatic Traffic Control System.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 1, page no.297-300, January 2020, Available :http://www.jetir.org/papers/JETIR1907P43.pdf

"Machine Learning Based Automatic Traffic Control System.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 1, page no. pp297-300, January 2020, Available at : http://www.jetir.org/papers/JETIR1907P43.pdf