• Parthipan R
• Mohammed Sameer
• Mrs Renuka Devi B

The waiting time in the queues and the behavior of people moving over queues makes standing in the queues a hard process so we are going to create an application that will detect all the people in the area and track the people who are joining a queue. The data collected from this detection and tracking process is used to train a machine learning model. A queue is actually measured by its appearance, size, arrival rate, service speed, and waiting time. The data collected from the tracking and detection of people are used to calculate the average waiting time and the busiest time of the queues. This will help the owner of the place to analyze the situation and make a decision like add or remove queues from the system.This application will serve as a business analysis model for the owner. For example: If there are 2 queues A and B. Queue A has 10 people waiting and Queue B has 5 waiting. Normally people will choose Queue B. But, using our Machine Learning model we came to know that the waiting time for queue A is 1 minute per person but for queue B is 3 minutes per person that is queue A will move faster by making this change we can manage the queue in a faster and efficient way.

Machine Learning, Object Detection

"CROWD MONITORING SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.166-168, April-2019, Available :http://www.jetir.org/papers/JETIRBF06034.pdf

"CROWD MONITORING SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp166-168, April-2019, Available at : http://www.jetir.org/papers/JETIRBF06034.pdf