Perhaps the camera operator's new work could be sitting in a comfy chair, tuning the areas of interest and motion profiles of a dozen cameras around the stadium to find the best angle on the action, allowing them to track smoothly under servos, pausing for a minute to think about the pacing of the game and what plays might develop, instead of mindlessly following the ball from place to place.
I'm an automation engineer, I've worked with hundreds of machine operators. They're almost universally happier after their employer gives them a better tool with which to do the work. The jobs that automated/AI equipment is replacing are not good jobs. Performing the same task for 90 minutes, much less an 8 hour shift, is awful on the body, and even worse on the mind. Humans don't make great robots, and it's not great to ask a human do a job that should be done by a robot.
By the way, is that your idea of an ideal job for everyone? Sitting in a "comfy chair", rather than doing, you know, the actual job of a cameraman, a job that is arguably an art form in itself? Do you understand that people love sports, people actually enjoy the athleticism of running around with cameras documenting sports, and that what you're dismissing would be a dream job for many people?
Do you really truly think the people buying this technology are doing it so that they can also continue to pay that same person to sit in a room and control it, while working towards self-actualization??
And do you think the appropriate response to concerns over jobs being eliminated is that the newly unemployed should feel grateful for being "freed" from the work they loved and took pride in, to go and do something "more fulfilling"? And add insult to injury by telling them that the job they had was just not a good job and wasn't something humans should have been doing in the first place?
> I'm an automation engineer, I've worked with hundreds of machine operators.
You mean, the ones that are keeping their jobs? Have you spent just as much time with the ones whose jobs have been eliminated?
I mean, I can understand that you may be facing a little cognitive dissonance here due to your job, but ... try to show a tiny bit of empathy for your fellow human. You think AI won't be able to do your job someday? Are you totally comfortable being dropped onto the labor market in today's economy if your current professional skill set became entirely valueless overnight, or is that something you only expect other people to be OK with?
We are recreating and eliminating entire industries, jobs, and traditions, and the level of awareness and respect for that disruption needs to dialed up by several orders of magnitude, or we as an industry, and all of us as a society, are in for some serious trouble.
Then you have to take the next step and ask: why do they do it? If it’s tedious, unsatisfying, lacks prestige, and is utterly demoralizing, what possible reason could somebody have to do it? Either the worker is insane, or else...
Most jobs in the world are not good jobs, sure.
But unless you also take money from the capitalists and elite laborers enriched by automation to provide stronger social safety nets, automating away demand for low-skill crap jobs while making the fewer remaining jobs both better and higher paying means increasing the prevalence of the worst human misery. People might hate crap jobs, but not as much as they hate being qualified for no job in a capitalist society.
In reality that person probably turned to the gig economy, with no job security, benefits, or upwards mobility, and works twice as much to put less money into savings.
Sometimes these debates on automation end up in a fight over how the economy of Star Trek works when you have a machine that can materialize almost anything. That's an interesting rabbit hole to dive into when you ask if any random person could just ask for an Enterprise type starship to be instantly generated. What are the constraints in that economy that make it not possible? Energy?
Historically, yes, but I think that's a fatally flawed analysis this time around.
It's important to consider the minimum skill level required by the market. Not all jobs require the same level of mental acuity. To date, automation has consistently removed lower skilled jobs and replaced them with higher skilled ones.
There are obviously physical limits to that process and I am convinced that we are rapidly approaching them. Take a look through the present day cutting edge results in machine learning, remembering that today's cutting edge is tomorrow's par for the course.
Some of that volatility was women entering the workforce in large numbers, but when you consider every percent represents millions of people it’s shocking how long disruptions can be.
Instead we are using the best and brightest to solve 'hard' problems like netflix suggestions and self driving cars.
There's only so many bullshit paper-pushing jobs and useless over-budget infrastructure projects we can create to make up for the lack of work.
A better way to deal with too much labor on the market would be setting government-mandated maximum work-hours, so as to spread the amount of available work evenly among the entire populace.
Secondly - for those countries where this isn't a reality yet - school and university should be free and you should receive government aid while attending them, to allow those out of a job to learn new skills or just pursue their interests.
Isn’t infrastructure chronically underfunded?
https://www.infrastructurereportcard.org/wp-content/uploads/...
I think you’ll find many of those actually want to work hard will bristle at that policy. Not to mention finding that hard (and undesirable IMO) to enforce on entrepreneurs.
TLDR: Often after tech makes bureaucracy paperwork easier, some bureaucrats create more paperwork to compensate that, because they want more control over what is going on the office.
Stalling progress in the name of "saving the jerbs" never works in the long run. Better to pull off the band-aid quickly and find solutions that don't require humans to continue doing jobs that can be done by machines.
All you need is to have the system set up so that it can be remote controlled by somebody away from the camera, if needed.
Imagine we don't have parfocal lenses and you need multiple cameramen with all different fixed focus lenses filming different parts of the scene, or some other contrived example of less technology creating a need for more jobs. That would surely be a better world by your standard. Should we try to eliminate some technology to create extra jobs?
Where is the correct balance? Do we currently have not enough cameraman jobs or too many? It won't be exactly the right amount by coincidence.
It's too it's all a zero sum game and the saved money will just be hidden under a mattress and not spent on anything else.
That said I don't agree. If you can afford the technology for this type of camera and the connectivity to get it online, you can afford to pay someone £20 to point the camera for a couple of hours. It's a great opportunity for people still in school to start, and far more fulfilling than stacking shelves.
This seems to be the disrupted version.
But I don't think we need to worry until the AI says "This is pointless and I have more interesting things to do."
And it's just bound to happen at some point.
I think the ML specialists often forget there's a gap between pattern matching and solving a problem
The problem here is: locating a ball and moving the camera
If you just try to find a ball on static image by static image you're going to have a bad time. Even worse if you try to find only the ball but without the play contexts (kicking, flying, being held, etc)
Balls "teleport" but not always
Yes, a ball can be on top of a player, but not all the time
So, no, if you didn't see the ball go there, it didn't suddenly appear in the middle of the field (but then again, with some exceptions).
This isn't the point you were making, but as an aside, I think converting video into static images is throwing away useful information.
Most video codecs work by encoding the changes between video frames which includes movement information. It seems hard, but worthwhile, to use this information for object recognition.
I just did a search and saw that these techniques go by the name "compressed domain analysis" [1].
[1] https://scholar.google.com/scholar?hl=de&as_sdt=0%2C5&q=comp...
Can be solved adding a "too-sexy" list. The fake ball was always in the same part of the field. When the problem starts, a human should be allowed to enter a command ordering to ignore the area around coordinates X,Y of the grid. Putting a virtual black rectangle in the area only to be seen by the camera. Coordinates could be passed as arguments. When the real ball enter this area the camera of course will lost it, but will quickly relocate it at the other side so is not much of a problem.
Their training data must have been seriously lacking.
But in defence of the software: this was a sideline ref, so probably a different kind of behaviour from the main one.
This system, to me, looks like it works on individual video frames (the camera seems to move away whenever the ball is even half-covered by a player’s body, or hard to see looking against the sun). A smarter system would know the ball doesn’t move 20 meters in a single frame, and tends to move faster than other objects on the field. That may not trivial to implement, but, I expect, would lead to a much more robust system.
It’s not like AI engineers manually run all varieties of footage against the trained model to check accuracy.
AI for controlling a football camera, sure, move fast and break things. Why not.
AI for controlling a moving motor vehicle, maybe we should be extremely cautious about our testing and use of AI.
No you wouldn’t. But it’s not.
“Move fast, break things” is perfectly ok for certain applications, such as recreational AI. Nobody said it’s ok for medical or military applications.
You can live in a world where the bar for quality control varies depending on the application.
Experimentation will never happen if we set medical/military grade expectations across the board.
Background: I was one of the original engineers on the Goal Line Technology system now used by the vast majority of professional leagues.
Heads are surprisingly ball shaped from a a lot of camera angles, and the male balding pattern conspires to give a good impression of different football graphical designs.
When we were developing the system, we thought we had ball detection pretty nailed, and we never had false positives from any of the heads of engineers on the team. Then, our boss came to test the system ... As far as our algorithms were concerned, his head was exactly a football, just as in the article! Highly embarrassing, but everyone saw the funny side. It gave us better data and inspired a few more robustness checks that ended up being crucial later on! :)
Even looking like half a sphere is enough, as the system attempts to detect partially occluded balls too.
Btw, funny that my comment above attracted so much hatred. Didn't want to sound like a know-it-all but yes, at least in the case of the referee I guess that checking for a body would have helped.
For example, in this stylised picture we can easily tell the player's head from the ball:
https://www.pngitem.com/pimgs/m/80-803112_italia-italia-90-l...
I can't imagine that the training and maintenance of such a system could be cheaper than the pay of a single camera person and I don't remember ordinary camera people having much trouble following the ball in soccer.
Which is to say, while current ML/AI may work well for some things, for other things, it will be abandoned as a fad unless there's some fundamental improvement.
Many countries have restrictions on games. No fans allowed and the personal supporting a game has to be reduced to an unsustainable low minimum with a very high-effort hygienic concept.
This may sound strange for americans but is the current norm in most countries. So if you are allowed less and less people as covid-19 gets worse you are in search of solutions. And as there are many camera men to record the game from different angles why not comply with these restrictions by replacing them with ai...
Eventually it will get better and cheaper than a human operator.
But it's like that with lots of things, for example humans.
It's like looking at a kid, and saying "it cannot even walk, it will never become a good cameraman".
We often hear that the AI is more accurate than humans in some image matching, but it also clearly makes mistakes that no human would make. Well here is a great example of such a mistake which a human wouldn't be making.
I can see a pattern. It helps if you think strictly in terms of what visual feature a ball and a bald head have in common: a crescent of glare against the sun.
* The ball is obscured by legs, shadow, or a player's body (this appears to be majority of the occurrences to me) - this one seems to trigger even if it's only out of view for a fraction of a second
* The high kick at 2:07 is similar to the above, except it's the glare from the sun instead of shadow that obscures the ball (ambient brightness matches the crescent instead of the umbra on the ball)
* Most of the rest I can't even find the ball myself, I think it might be too small in the far end of the field to register clearly
I think it is a testament to not just human complexity, but human efficiency.
I mean how would it be for a AI to take in a score and output "they played well"...
https://techcrunch.com/2010/11/12/automated-news-sports-stat... https://en.wikipedia.org/wiki/Automated_Insights
“Did you see that ludicrous display last night? ...”
We have since then developed ways of analysing the inner workings of neural networks.
You can understand how each feature drives the predicted result by using SHAP https://github.com/slundberg/shap
You can analyse the individual layers and gain an understanding of how each layer encodes the input
https://ai.googleblog.com/2017/11/interpreting-deep-neural-n...
https://www.kdnuggets.com/2019/07/google-technique-understan...
Do we 'know' in an absolute way why a network thinks something? Not yet. But neither do we know that for humans. We just have a huge amount of experience with their failure modes and we work around them (see aviation).
https://en.m.wikipedia.org/wiki/FoxTrax
I suspect in this instance it wasn't just the bald head, but the particular near-sunset lighting that was a contributing factor.
Great example of first iteration tech being the tip of the iceberg.
What happens if there are multiple balls? What happens if the ball is redesigned with a different pattern? What happens if someone wears a shirt with a picture of a ball?
Does it make sense to center the camera on the ball? Does it make sense to also show where the ball can go?
I wonder how it's handled by people currently? I suppose it can be handled with more training data/labelling instead of custom coding.
We are looking are replacing the camera person job with AI just as we replaced other people's jobs with programming earlier and just as we replaced other people's jobs with AI more recently.
There were loads more, but that was off the top of my head. I dont think we ever mistake a head for a ball though! We worked out likely balls from movement (syncing cameras with bounces)
(Or maybe you won't even need to rent the camera, if you use the free version supplied by Google which automatically adds personalized sponsorship visuals)
(Or the Facebook version, automatically posting 'hilarious' and embarrassing snippets designed to maximize user engagement)
Latency: (cheap labour is usually far away from the match). This makes teleoperation harder.
Bandwidth (TV ops run from a van in a car park - so getting a reliable video feed out is expensive).
Logistics: you still need to hire, train and utilise that person effectively.
Reliability: what if your remote operator's internet goes down?
Science is made better mistake after mistake. Better in a football game than in a shuttle, or a law enforcement AI.
At least they can wear hats. If you are black and rely on face detection you are out of luck.
