So, I’m about the finish college. I’ve been majoring in Computer Engineering, a 5-years course (Engineering courses are 5-years long in Brazil) and one of the last things you do before saying bye bye college life is a “TCC”, like a final year project, but normally in 6 months (if you’re lucky). I’ve been meddling with
dark magic Machine Learning since last year and I had in mind I wanted to do something related to that subject. Moreover, I’ve had good experience with Embedded Systems before and wanted to do something with that as well. Machine Learning tasks normally take a lot of time and space, which is something that embedded system do not have to spare, therefore I was playing with fire…
I’ve approached one of my College’s professors at the beginning of the semester (he’s normally an Embedded Systems guy, but he’s got some knowledge in AI too) and asked for his help. In Machine Learning I wanted to work with Convolution Neural Networks (CNNs) because I’ve heard of their power before and wanted to experiment with it. TL;DR He proposed we’d studied CNNs involving detecting people in aerial images. It’s a research that has been done before so we’d need to tweak it a little bit to make it unique. We decided to study about its use in embedded systems and maybe make a comparison with another famous image classifier (HOG + SVM). So this is the master post where I’ll try to sum up our experiments and observations related to that problem. Hopefully this post might help somebody who’s trying to learn more about the same subjects. I’ll try and divided it into 7
horcruxes parts, so when I’m finished with each part I’ll post a link to the post right here.
- Exploring HOG and SVM
- Exploring CNNs
- Caffe Framework – Installation
- Caffe and CNNs
- HOG+SVM Embedded
- CNNs Embedded.
- Final Post.
That’s it for now. Stay wise.