| | 1 | = Smart Intersection - daily traffic flow = |
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| | 3 | == Project Objective == |
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| | 5 | The goal of this project is to create a method for estimating the statististics for vehicle count/traffic flow into one intersection in New York City. As an example, record videos of the northbound traffic on Amsterdam Avenue, as vehicles are entering the 120th St./Amsterdam Av. intersection. Using Yolo V3 deep learning model, detect and count vehicles as they approach/enter the intersection from south, making sure that there is no double-counting. Use 180 second long video fragments (approximately two traffic light cycles), and repeat up to half a dozen times a day, for a number of workweek/weekend days during the same times of each day. Compare the vehicle count (traffic flow) as a function of the time of the day. Utilize Nvidia Deepstream deployed on COSMOS GPU compute servers to run the model. The method should be generalizable/expandable to any direction of vehicle movement, when appropriate camera views are available. |
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| | 7 | == Reading Material == |
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| | 10 | == Week 1 Activities == |
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| | 12 | * Learn about YoloV3 deep learning models for object detection |
| | 13 | * Read about NVIDIA deepstream |
| | 14 | * Explore the image (set of computing tools) available on COSMOS, which uses deepstream and can deploy YoloV3 |
| | 15 | * Record and save 6 videos during one day (to be repeated when the method is debugged and fully functional) |
| | 16 | * Brainstorm about vehicle counting/traffic flow estimation methodology |
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