ECE497 Project - Object Detection w/ DNN
Team members: [Paul Wilda, Leela Pakanati]
I'm using the following template to grade. Each slot is 10 points. 0 = Missing, 5=OK, 10=Wow!!
00 Executive Summary 00 Installation Instructions 00 User Instructions 00 Highlights 00 Theory of Operation 00 Work Breakdown 00 Future Work 00 Conclusions 00 Demo 00 Late Comments: I'm looking forward to seeing this. Score: 10/100
GET PICTURES OF IMAGE RECOGNITION
We are using tensorflow and OpenCV to detect items in the frame of a web camera. The camera is mounted onto a tilt pan kit to allow us to track the objects in frame as well. Due to the intensive nature of the object detection, we are using a local web server to process the image and find the objects within it. The web server returns an error vector which the Pi coverts to a control vector. It can then adjust its angle to keep the tracked object in the middle of the frame. In order to dramatically decrease the complexity of the project, we would have liked to preform all the processing on the Pi as well however we were unable to get a reasonable response time with either the Pi or the BeagleBone.
If you have hardware, consider Small Build, Big Execuition for ideas on the final packaging.
Give step by step instructions on how to install your project.
- Include your github path as a link like this to the read-only git site: https://github.com/MarkAYoder/gitLearn.
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- Include kernel mods.
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Once everything is installed, how do you use the program? Give details here, so if you have a long user manual, link to it here.
Here is where you brag about what your project can do.
Include a YouTube demo the audio description.
Theory of Operation
Give a high level overview of the structure of your software. Are you using GStreamer? Show a diagram of the pipeline. Are you running multiple tasks? Show what they do and how they interact.
Getting OpenCV on Pi/Beaglebone (10/28)
Testing Pi vs Beaglebone operation (10/29)
Image sending and receiving (11/5)
Web server configuration (11/5)
Servo and tilt pan kit assembly (11/10)
Control loop and tuning for servos (11/14)
Enclosure design and construction (11/16)
Creating our own libraries to train our model on would be a very interesting addition to this project. This would allow us to detect and recognize individuals and only track certain people.
Making the tilt pan kit more robust would allow us to mount a nicer camera to the system and would significantly improve the image quality as well as the recognition accuracy.
Give some concluding thoughts about the project. Suggest some future additions that could make it even more interesting.