Smart City Gnosys
Smart city article details
| Title | Smart Traffic Management System Using Deep Learning For Smart City Applications |
|---|---|
| ID_Doc | 51587 |
| Authors | Lingani G.M.; Rawat D.B.; Garuba M. |
| Year | 2019 |
| Published | 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019 |
| DOI | http://dx.doi.org/10.1109/CCWC.2019.8666539 |
| Abstract | Already known as densely populated areas with land use including housing, transportation, sanitation, utilities and communication, nowadays, cities tend to grow even bigger. Genuine road-user's types are emerging with further technological developments to come. As cities population size escalates, and roads getting congested, government agencies such as Department of Transportation (DOT) through the National Highway Traffic Safety Administration (NHTSA) are in pressing need to perfect their management systems with new efficient technologies. The challenge is to anticipate on never before seen problems, in their effort to save lives and implement sustainable cost-effective management systems. To make things yet more complicated and a bit daunting, self-driving car will be authorized in a close future in crowded major cities where roads are to be shared among pedestrians, cyclists, cars, and trucks. Roads sizes and traffic signaling will need to be constantly adapted accordingly. Counting and classifying turning vehicles and pedestrians at an intersection is an exhausting task and despite traffic monitoring systems use, human interaction is heavily required for counting. Our approach to resolve traffic intersection turning-vehicles counting is less invasive, requires no road dig up or costly installation. Live or recorded videos from already installed camera all over the cities can be used as well as any camera including cellphones. Our system is based on Neural Network and Deep Learning of object detection along computer vision technology and several methods and algorithms. Our approach will work on still images, recorded-videos, real-time live videos and will detect, classify, track and compute moving object velocity and direction using convolution neural network. Created based upon series of algorithms modeled after the human brain, our system uses NVIDIA Video cards with GPU, CUDA, OPENCV and mathematical vectors systems to perform. © 2019 IEEE. |
| Author Keywords | Computer vision; Convolutional Neural Network; Counting; Deep Learning; Detection; GPU; Intersection; Neural network; Tracking; Video |
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