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I have trouble believing there's enough information content present in CCTV streams to uniquely identify individuals confidently. I see how it maybe could work, but it's not something I'd focus on directly. Are human gaits really that different as to be identifiable from distant security cameras? Are they even consistent for a single person day-to-day?
The longer I think about it, I've also started thinking that AI likely scales sub-linearly (logarithmic?) with the size of the training dataset. "But the AI can viably consider a larger dataset than human experts" may be true, but may not generate hugely better results.
Interesting! Any good papers or summary articles you'd recommend?
Sure, there's a decades-long history of forensic gait analysis (long predating AI of course) in criminology. A nice overview is here. It's actively employed in China integrated with AI, although not widely in the West. In the West, gait analysis by experts has been a feature of trials for a long time - even before CCTV, it was used (and still is) on footprints left at crime scenes to identify suspects.
The challenge, of course, is that for now the applications of current forensic gait analysis are highly limited. The lack of comprehensive gait libraries for the wider population means that, unlike DNA (at least in recent memory) it's generally only used to support attempts to prove a suspect on trial was or was not someone in video footage. The real benefit is in scanning a library of millions or billions of hours of video taken from a network of surveillance cameras (which have ideally already been used to build up a 'library' of the entire population) to find possible 'matches' (the search space can be narrowed by geography and other quantitative or qualitative information recorded by police) in the general public, just like police DNA databases and Ancestry.com data are today.
In the West, research has been slow for a while. It's generally focused on identifying diseases like Parkinsons, the racing industry uses it for analysing horses etc, so a lot of Western research uses Lidar and multiple cameras; these achieve extremely high accuracy (often over 90%), but obviously aren't hugely helpful when the footage is actually blurry black-and-white CCTV at night.
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