Slashdot Mirror
Google Research Leads To Automated Real-Time Pedestrian Detection
An anonymous reader writes with a link to a story about one of the unexciting but vital bits of technology that will need to be even further developed as autonomous cars' presence grows: making sure that those cars don't hit people. Google researchers have recently presented findings about a method that tops previous ones for real-time pedestrian detection using neural nets "that is both extremely fast and extremely accurate." From the article: There are other approaches that provide a real-time solution on the GPU but in doing so, have not achieved accuracy targets (in this real-time approach there was a miss rate of 42% on the Caltech pedestrian detection benchmark). Another approach called the VeryFast method can run at 100 frames per second (compared to the Google team's 15) but the miss rate is even greater. Others that emphasize accuracy, even with GPU acceleration, are up to 195 times slower.
No. As usual, the summary is confusing as it gives numbers for the *older* methods, but the current Google's method is: "The resulting approach achieves a 26.2% average miss rate on the Caltech Pedestrian detection benchmark, which is competitive with the very best reported results. "
So, there's 26.2% chance that on a single particular image, you miss the pedestrian (at the same time, it seems that in about 15-20% images it sees a pedestrian that is in fact a shrubbery or whatever). This is an academic dataset, and in reality you will have a video feed. AFAICS it's not clear how the precision translates when you have a sequence of many pictures of the pedestrian - whether you will have much higher chances to spot them at least on some of them, or if it's more of a systematic problem and khaki-clothed people just don't stand a chance.
It's not the fall that kills you. It's the sudden stop at the end. -Douglas Adams