1. CRCV REU
UCF Summer 2019
Arisa Kitagishi

2. The first week: Python Tools and Framework Deep Learning and CNN
Anaconda3
Keras
Tensorboard
GPU/ Google Colab
Deep Learning and CNN
I had an experience with Python before, but it was still very helpful to go over the Python with the group and learn new things within Python.
I actually have not used Anaconda3, Keras, Tensorboard, or GPU before, so it took me a bit to install and ensure my environment was working.
I learned a lot to use them comfortably by the end of the week and was able to build a CNN sucessfully.
It really helped that we went over a lot about CNN and deep learning such as loss functions, activation functions, and hyperparameters so that we can construct and play around with them using the CNN we constructed. My CNN didnt do particularly well, but I learned a lot on how layers may not necessarily make your model better, having dropouts a lot is not a good thing, and so on.

3. The second week: Pytorch MobaXterm and Newton More CNN Applications
Conv3D
Inception Module
Applications
By the second week, we started learning Pytorch and set up Newton.
We also learned about the 3D convolution as well as I3D and inception module.
2D convolution would be using spatial and 3D convolution is both spatial and temporal. I was able to image this better with the explanation of Robert and his diagram he found.
I also learned that common CNN to use for videos is I3D which consists of this inception model.
We went over and coded for the I3D with Pytorch, andI learned that Pytorch is much harder compared to Keras, but it has much flexibility for us to adjust the CNN which is nice imo.
I also thought it was nice to see different research papers in computer vision and appreciated all the presentations because it was really nice to see how the things we learned are applied to different fields such as object detection or tracking.

4. Research:
1. Moving Target Detection with Infrared Sensors - Dr. Mahalanobis and Babak
The research I chose to work for this summer is Dr. Mahalanobis and Babak’s research of Moving target detection with Infrared sensors.
The research is about detecting a target such as vehicles or human that are more than 4km away using images obtained via infrared sensors. Some of the problems they are tackling are how to calculate the number frames to skip as it correlates to distance, speed, and time.
I chose this research because I saw great potential for this research to expand even if I continue to stay after this summer. I also liked how we could differentiate and point out small details that our eyes may not be able to and became interested in how we can possibly create CNN or analyze this dataset. Such as solving for how many frames we should skip depending on how far the object is and its speed. How we may differentiate the target from the false alarms as the background moves as well. Dr. Mahalanobis also mentioned the possibility of changing Dataset to further this application. I felt that I will gain a lot from this research that I could apply for my future career.

5. Thank you!