• Shashikala J
• Sagar H
• S Tejas Narayan
• Sanjith R
• Rohit B C

This paper describes the various technologies which are employed in Real Time Vehicle Detection using an edge device Intel Movidius Neural Computing Stick. Vehicle detection is a very important task which enables the smart traffic management here moving from cloud centric to edge centric approach greatly minimizes the bandwidth and latency issues. The MobileNet-SSD an ImageNet classification model is a light weight architecture built on a Caffe framework ensures better performance finally implementing the model on low cost high performance oriented Raspberry Pi will greatly minimizes the cost and easy deployment of model thus Real Time Vehicle Detection is achieved

Neural Computing Stick (NCS), MobileNet-SSD, MYRIAD architecture, SHAVE VLIW vector processor, LEON Microprocessor, Raspberry PI.

"Survey Paper on Real Time Vehicle Detection on Edge for Smart Traffic Management", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.229-232, May-2019, Available :http://www.jetir.org/papers/JETIRBM06036.pdf

"Survey Paper on Real Time Vehicle Detection on Edge for Smart Traffic Management", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp229-232, May-2019, Available at : http://www.jetir.org/papers/JETIRBM06036.pdf