Traffic Light Recognition using Deep Learning

Traffic Light Recognition using Deep Learning

Vamshi Gudavarthi

Vamshi Gudavarthi

Lakeside, California

Recognizing Traffic lights in the driving direction using Deep Learning. The dataset contains images taken by the drivers using Nexar app.

Artificial Intelligence

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This project is pursued as a final project for the course Neural Networks and Pattern Recognition taken at University of California, San Diego. We tried to solve the problem posed in Nexar challenge shown in the following link, ( Although the challenge is over by then we are able to solve to the extent we were almost there matching the 2nd rank shown in their leaderboard. Also we localized the region of traffic lights using UCSD Traffic lights dataset where data for bounding boxes is also available. Apart from this I had many other machine learning and deep learning projects to my credit. Also I am currently enrolled for graduate study at UCSD specializing in Intelligent Systems, Robotics & Control. Following is one of the reference we used for our project (Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun)

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Farm Assist

With everything going digital, there’s need for a digital platform that allows farmers to automate their farm activities such that they have visibility of the farm progress anytime anywhere on single click. Coming up with a digital online platform, Farm Assist (FA), an internet application software, will help farmers to solve these problems in a more efficient and less costly way leading to increase in production.

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Tushaar G. updated status

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Tushaar Gangarapu

Hello everyone, I am Tushaar. I am currently pursuing my bachelors at NITK. I developed passion towards AI and Machine Learning, had mentored the same at a mentorship program and currently was working on "Real Time Sensor based Weather Analysis and Prediction using Cloud Analytics to Improve Agricultural Yield". At the moment all we have are basic regression models which actually work with an 80 percent efficiency approximately. The repositories related to both the mentorship program and the regression models can be found on my GitHub account.

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Data Stream Mining

If the application data is very large and the recent data is the most important, by using data stream mining techniques we can extract knowledge to facilitate and simplify decision making. This project intends to study data streaming mining in internet networks, IoT sensors and social media. The machine learning algorithms need modifications to be adaptive and incremental. Modern code is also required to optimize computational resources.

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Bob Duffy

Folsom, CA, USA