Ground truth and algorithms

On Saturday, Daniel and I worked on a couple of really important algorithms:

1. An algorithm to combine blobs that are very close together/closely related--this is necessary to work around the poor performance of OpenCV's blob detection functionality in poor lighting conditions.
   
2. An algorithm to track blobs across frames, assuming that algorithm 1 was run on each frame. This is so we can see the trajectory of people blobs towards different cars.

Speaking of people and car blobs, I also took some additional video on Saturday from the AP&M building (looking at the Faculty Club lot). Using Nick True's labeling program, I labeled several frames from that video. They're below:

groundtruth1.png
groundtruth2.png
groundtruth3.png

(car blobs are red, people blobs are green)

From the looks of things, car blobs and people blobs are extremely similar in size, at least from that vantage point. Simply looking at the size of each won't be enough, unless the sizes of each blob are clearly distinguishable. Perhaps we can track each blob and use its rate of movement to determine which category a particular blob belongs to.

Also, OpenCV's blob detection library seems slow for real-time use (70-90ms per frame according to gprof). For the time being, we won't worry about detecting in real-time.