Ankita’s Status Report for 3/23/24

Work Done

I finished tagging the positive and negative images needed to train the vehicle classifier from traffic camera footage as well as the footage Zina retrieved for me from the Fifth and Craig intersection. Unfortunately the commands I would use to train the classifier have since been deprecated, so I ended up using Anaconda to set up a virtual environment and installed an older version of OpenCV through that so I could run the commands. I was ultimately able to train a classifier, but it has pretty terrible accuracy so I think I need to revisit how I’m collecting the data. Most of the tutorials I found online have been using way more samples than what I’ve amassed (on the order of hundreds rather than just 60, which is what we currently have), so I think we need to go back to the drawing board in terms of data collection. Either that, or we should go with a more accurate model (YOLOv4). Also, tagging the images takes a lot of time (for the positive images, bounding box coordinates of each object in the frame need to be specified) so I may need longer to do this than initially anticipated.

I tried to access the IP camera video feed from the Raspberry Pi using RTSP, but after some difficulties I found out that Reolink’s battery-powered cameras don’t actually support RTSP streaming (source.) This means that we won’t be able to access the stream outside of Reolink’s app, so I looked into alternative wireless cameras that do support RTSP streaming and found these options: Amcrest and MubView. If these don’t work, we will probably resort to using prerecorded footage.

Schedule

I need more time to get the car detection model trained. For the pedestrian detection model, I will use a pretrained classifier (even if the accuracy is not necessarily up to our metrics) because all we need to know is if there are pedestrians at the intersection.

We also need to wait for the new camera to come in so that adds some delays to our integration as well.

Deliverables

By the end of next week, I will:

• Train a (hopefully) better vehicle classifier after amassing more positive/negative samples and tagging them
• Order the new camera ASAP