This week, my main goals were to look into preprocessing for the model, to decide the type of data to collect and to start data collection. This changed a bit after Monday once the question around image subtraction as an alternative to deep learning-based object detection came up. I didn’t have much knowledge of non-deep learning methods, so I switched gears a bit to research more into it. I found that what we are looking for is background subtraction and looked into different feature detection methods and classification models we could use in tandem with it.
I went through a textbook for a bit to learn more about non-deep learning based object detection and some comparative studies as well. I mainly found HOG, SIFT, SURF, ORB and BRISK. So far I am finding that ORB and BRISK seem to have a good tradeoff between computational complexity and being able to pick up features in an image. Out of classification models to run on the feature extraction output, Naive Bayes and SVM, along with a few others are popular. As far as data collection, I found a dataset we can use preliminarily to train the model before moving into using footage of the study space itself.
With some midterms and coming up, I didn’t get to work as much to get a model of one of these systems up and running, so I’ll be getting to that next week along with starting data collection of the study space itself.