Ankita’s Status Report for 2/24/24

Work Done

This week, I prepared for and gave the design review presentation for my group. I also made some progress on the car detection code, but I realized that we will probably need to train our own Haar cascade, since the ones I found online don’t perform very well on settings where other buildings are in frame. In the screenshot below it’s evident that the classifier is fairly accurate:

But in this one, the classifier struggles to differentiate the buildings from the cars.

We’ll obtain some videos from local intersections with the camera angles that we’ll actually be using and test the pre-trained classifier on those; if we get similar lackluster performance, I’ll start training a separate one.

I also started the Raspberry Pi and IP camera setup; the IP camera setup is fairly intuitive as it uses an app interface to connect to WiFi (I haven’t yet attempted to connect it to CMU-SECURE, but got it to connect to my apartment’s WiFi.) The Raspberry Pi that we borrowed from the capstone inventory seems to have already been registered to CMU-DEVICE under an unknown hostname, so I’ve contacted IT to help us get that resolved (since we can’t ssh into it if we don’t have the hostname.)

Schedule

Because both the vehicle classifier and the Raspberry Pi setup are taking longer than usual, I am behind schedule as these should have been done by this week. I will keep working on the vehicle classifier through next week and spring break, and we may reduce our accuracy metrics for both the vehicle and pedestrian object counts if we continue to see issues with the detection code and repurpose a good portion of the vehicle detection code for the pedestrian detection code (since that’s what’s next on my schedule.)

Deliverables

By the end of next week:

• SSH into the Raspberry Pi and try to set it up as a hotspot for the IP camera to connect to.
• Get video footage of local Pittsburgh intersections and train new Haar cascade classifier for vehicle detection