Latest Analytics? Tag And Trace / Track?

Would not describe M&A activity as hot. VideoIQ was the 'best' or at least one of the best and it did not sell for much. The only really impressive deal (from a valuation perspective) was the OV patents, and that was because Avigilon sees money in enforcement / litigation of patents.

2, most of our analytic tests are on 'virtual trip wire' / cross line / whatever marketing term people use, e.g., Axis Video Analytics Are Weak, Avigilon Analytic Cameras Tested, etc.)

What is tag & trace? Is that your term? A vendor marketing term? It's not a commonly used one in the industry.

"be keen to know if this is still considered 'leading edge'or has it become mainstream?"

Not leading edge, not mainstream. It's basically a clever marketing trick, more appropriately science fiction.

You can stage something like this, but it will not work reliably in production. These videos are really irresponsible.

Are there people out there having success with 'tag and track' searches in crowded scenes such as train stations, airports, large busy shopping malls?

No, they are not.

There are several problems with getting something like this to work in real life. A few key things that make it hard to do this in any manner that an actual customer would be happy with:

1) White-balance and color saturation across multiple cameras. As you start to cover the kinds of scenes you mentioned (train stations, malls, city areas), you run a very high probability of having white balance and lighting affect the overall appearance of the person. The "red jacket" the suspect is wearing might be bright red in one scene, dark burgundy in the next and so forth. The color difference across the cameras may also not be consistently predictable, such as any camera that gets natural light instead of 100% indoor even lighting. This makes it very hard to do even basic cross-camera tracking.

2) Object detail. Using facial recognition is completely out of the question, no system has the pixel density to use face-rec, and that only works from the front anyway. A normal camera system won't have enough pixel density across the entire image for things like this to work well. Customers are reluctant to deploy cameras that would have the detail required to really make this feasible (assuming it existed in the first place, which it does not).

3) Lack of object classification. The majority of the anlytics systems out there do not have any true object classifier. The majority of their categorization is based on simple scaled size and shape metrics. Because of this they don't really know when they are looking at a person, or just a large upright object. As a subset of this they also don't know when they are looking at a full person, or just an upper torso of a person (like you'd often have in a crowded scene). This makes it hard to define what the object looks like. Can we look for a guy in a red jacket and yellow pants, or are we only going to see the red jacket? What if we start with the red jacket and in a later image see that he has yellow pants? Can the algorithm recnogize that it is the same person, so that we don't start tracking the wrong person?

4) General lack of diversity in a large crowd. This is especially true in colder climates where a lot of people wear outer jackets that are frequently blue, black, or brown (or minor shades thereof). Unless the person being tracked has some VERY unique feature, there is a high probability as you look across cameras that you see additional people with a similar-enough appearance to be confusing. This is amplified by point #1 above.

5) Shifting object appearance. This kind of goes with #3 above. Camera FOVs tend to vary greatly, so the person doesn't have a consistent profile appearance. When the system has trouble even knowing positively that the object is a person (or not), it's even more difficult when you a clear fully-body profile shot in one view, and then a steep overhead shot in another view.

Unfortunately the large multi-camera sites that would probably most benefit from a function like this are exactly the kinds of scenarios that are guaranteed to make it impractical to implement.

You don't hear much about this application in mainstream analytics because it's not practical to implement and deliver reliably. It's not worth the frustration and support costs that come with it when there are more realistic analytics applications.