Setting camera traps in your garden to see what local wildlife is around is quite popular. But [Chris Lam] has just one subject in mind: the hummingbird. He devised a custom setup to capture the footage he wanted using some neat tech.
To attract the hummingbirds, [Chris] used an off-the-shelf feeder — no need to re-invent the wheel there. To obtain the closeup footage required, a 4K action cam was used. This was attached to the feeder with a 3D-printed mount that [Chris] designed.
When it came to detecting the presence of a hummingbird in the video, there were various approaches that could have been considered. On the hardware side, PIR and ultrasonic distance sensors are popular for projects of this kind, but [Chris] wanted a pure software solution. The commonly used motion detection libraries for this type of project might have fallen over here, since the whole feeder was swinging in the air on a string, so [Chris] opted for machine learning.
A RESNET architecture was used to run a classification on each frame, to determine if the image contained a hummingbird or not. The initial attempt was not greatly successful, but after cropping the image to a smaller area around the feeder, classification accuracy greatly increased. After a bit of FFmpeg magic, the selected snippets were concatenated to make one video containing all the interesting parts; you can see the result in the clip after the break.
It seems that machine learning and wildlife cams are a match made in heaven. We’ve already written about a proof-of-concept project which identifies different animals in the footage when motion is detected.
Continue reading “Hummingbirds, 3D Printing, And Deep Learning”
Housed on a standard-sized British electrical fascia was a 12-position rotary switch, marked with letters A through L. An unexpected thing to see in the 21st century and one probably unfamiliar to most people under about 40, I’d found something I’d not seen since my university days in the early 1990s: a Rediffusion selector switch.
