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Been there, done that. Put that car at an angle and see how well that works. Or maybe add rain. Or bright sunshine. Or make it dark. Spoiler alert: these kinds

Been there, done that. Put that car at an angle and see how well that works. Or maybe add rain. Or bright sunshine. Or make it dark. Spoiler alert: these kinds of image processing Rube Goldberg machines fall apart real quick with real world conditions. > The intent of this article is to sensibilize the reader that a machine learning approach in not always the best or first solution to solve some object detection problems. The danger of this article is people end up wasting time & budget with brittle approaches. (Though to be fair the danger of the deep learning/ML route is people can end up wasting just as much time when they don’t have an adequate amount of training data.)

neetdeth 8y ago
There’s a pretty vast gulf between 70s era threshold, edge detect, morphology type methods and deep learning. Robust feature detectors, voting methods, etc. can fill that gap for object detection and pose estimation. Also simpler machine learning models for classification like SVMs. It increasingly feels like the slice of computer vision techniques I learned in the early 2000s is receding in relevance. But at the same time, there ought to be a more modern grab bag of robust tools to build engineered solutions. In the end any algorithm is only as good as the dataset it’s developed/validated against. Ad-hoc methods may fail in bright sunshine, but so too would a neural network if that condition never arises in its training set.
- trentlott 8y ago
  The future of computing is here! After tiring of convoluted web 3.0 frameworks and Babel-esque dependency structures, programmers have finally fashioned a black box they cannot understand which relieves the anxiety of knowing they should.
  - king_magic 8y ago
    Do you know how your brain recognizes objects in images? Not superficially, but truly understand how it works at a deep fundamental level? I'll wait. Until then, when it comes to the state of the art in things like reading license plates, it's the old algorithmic approaches (like described in the article) that result in convoluted, brittle, easily broken approaches - far closer to the "convoluted web 3.0 frameworks and Babel-esque dependency structures" you seem to despise. DL may not be fundamentally understandable (yet), but it works a hell of a lot better than stringing together image processing algorithms.
yread 8y ago
Couldn't you use this approach to generate better inputs for the ML so that it's less complicated? The convolutional nn ocr linked below needs 450 million parameters!
- AstralStorm 8y ago
  Machine learning combined with parametric models is old, works and is not sexy tech so it does not get talked about.
michjedi 8y ago
The first thing I thought about was - what if a tiny part of the plate is cut off by e.g. the side of another car? It would get the ratio entirely wrong and think it was anything but a plate!
raverbashing 8y ago
You're right, though these techniques can be good as a starting step in an image processing pipeline. But to do OCR you need ML. Not "100 layer DL CNN using the latest arxiv paper" but a shallow learning technique should be enough. Those techniques could also be useful for identifying the license plate.
- malux85 8y ago
  Not really. The algorithms described in the article are pretty worthless (edge, blob detector) until the very last step, where they use the width / height ratio to filter out all of the noise and get the license plate out. As the parent comment said, this doesn’t work in the real world because of transalation/rotation. You can spend ages tweaking parameters manually, but the CNN will beat you - actually we have discovered that two DL models work best, one to locate the license plate, then the other to OCR (the former is faster computationally) The methods described in the article are dated, and worthless Disclaimer: I build number plate recognition systems
  - raverbashing 8y ago
    Maybe not exactly those techniques but I've heard of non-ML techniques being used in real life situations incl. translation/rotation to identify the plate (not read the numbers, just identify the plates) Edit: They are dated and might have made sense some 8 years ago which is when I heard of them being used
  - tracker1 8y ago
    While not ideal for the described scenario, if you are say scanning from a pool of limited form templates, it could be very useful and faster than ML methods, at least for an earlier pass. But this is a 2D scenario even then.
    - malux85 8y ago
      Marginally faster.... maybe. A forward pass on our two neural nets is less than 1.5 seconds. Is embarrassingly parallel, and has a much higher accuracy. In this task accuracy is much much more important than computational efficiency, and given a forward pass is already quick (less than 2 seconds) why wouldn’t you use the superior method that always works?
dingaling 8y ago
I've never been involved in ML, so when you talk about 'training data' does that require feeding known license plates in various conditions into the software and having it extrapolate between them to cover unseen cases? And doesn't it fail just as hard if it encounters a real-World example that it can't fit into its model? Probably a very naive question but this is entirely outside my domain.
oliwarner 8y ago
While your examples are accurate for hand-held or casual ANPR, they're easy to mitigate against when you're planning a fixed installation. Just look at existing ANPR installations. You get tight lanes. Barriers to arrest cars. High and low angle cameras working together. Heavy use of IR illumination (reads through all but the thickest mud IME). That's not to say some people don't get it wrong. I've worked on systems where they've cheaped-out and put a single camera at bumper-level. It's completely ineffective 2-4pm in winter because of the glare. But yeah, if you can avoid being a cheap idiot, you can easily take a system to 90+% confidence.
- king_magic 8y ago
  Right but it’s trading an image processing Rube Goldberg machine for a hardware Rube Goldberg machine.
24gttghh 8y ago
>Put that car at an angle and see how well that works. Or maybe add rain. Or bright sunshine. Or make it dark. Why would the car be at an angle? These type's of systems are usually directly overhead of a lane of traffic I would imagine; like at a tollbooth. The rest of that would be the job of the camera itself to best expose the image, no?

neetdeth 8y ago

There’s a pretty vast gulf between 70s era threshold, edge detect, morphology type methods and deep learning. Robust feature detectors, voting methods, etc. can fill that gap for object detection and pose estimation. Also simpler machine learning models for classification like SVMs. It increasingly feels like the slice of computer vision techniques I learned in the early 2000s is receding in relevance. But at the same time, there ought to be a more modern grab bag of robust tools to build engineered solutions. In the end any algorithm is only as good as the dataset it’s developed/validated against. Ad-hoc methods may fail in bright sunshine, but so too would a neural network if that condition never arises in its training set.

trentlott 8y ago
The future of computing is here! After tiring of convoluted web 3.0 frameworks and Babel-esque dependency structures, programmers have finally fashioned a black box they cannot understand which relieves the anxiety of knowing they should.
- king_magic 8y ago
  Do you know how your brain recognizes objects in images? Not superficially, but truly understand how it works at a deep fundamental level? I'll wait. Until then, when it comes to the state of the art in things like reading license plates, it's the old algorithmic approaches (like described in the article) that result in convoluted, brittle, easily broken approaches - far closer to the "convoluted web 3.0 frameworks and Babel-esque dependency structures" you seem to despise. DL may not be fundamentally understandable (yet), but it works a hell of a lot better than stringing together image processing algorithms.

yread 8y ago

Couldn't you use this approach to generate better inputs for the ML so that it's less complicated? The convolutional nn ocr linked below needs 450 million parameters!

AstralStorm 8y ago
Machine learning combined with parametric models is old, works and is not sexy tech so it does not get talked about.

michjedi 8y ago

The first thing I thought about was - what if a tiny part of the plate is cut off by e.g. the side of another car? It would get the ratio entirely wrong and think it was anything but a plate!

raverbashing 8y ago

You're right, though these techniques can be good as a starting step in an image processing pipeline. But to do OCR you need ML. Not "100 layer DL CNN using the latest arxiv paper" but a shallow learning technique should be enough. Those techniques could also be useful for identifying the license plate.

malux85 8y ago
Not really. The algorithms described in the article are pretty worthless (edge, blob detector) until the very last step, where they use the width / height ratio to filter out all of the noise and get the license plate out. As the parent comment said, this doesn’t work in the real world because of transalation/rotation. You can spend ages tweaking parameters manually, but the CNN will beat you - actually we have discovered that two DL models work best, one to locate the license plate, then the other to OCR (the former is faster computationally) The methods described in the article are dated, and worthless Disclaimer: I build number plate recognition systems
- raverbashing 8y ago
  Maybe not exactly those techniques but I've heard of non-ML techniques being used in real life situations incl. translation/rotation to identify the plate (not read the numbers, just identify the plates) Edit: They are dated and might have made sense some 8 years ago which is when I heard of them being used
- tracker1 8y ago
  While not ideal for the described scenario, if you are say scanning from a pool of limited form templates, it could be very useful and faster than ML methods, at least for an earlier pass. But this is a 2D scenario even then.
  - malux85 8y ago
    Marginally faster.... maybe. A forward pass on our two neural nets is less than 1.5 seconds. Is embarrassingly parallel, and has a much higher accuracy. In this task accuracy is much much more important than computational efficiency, and given a forward pass is already quick (less than 2 seconds) why wouldn’t you use the superior method that always works?

dingaling 8y ago

I've never been involved in ML, so when you talk about 'training data' does that require feeding known license plates in various conditions into the software and having it extrapolate between them to cover unseen cases? And doesn't it fail just as hard if it encounters a real-World example that it can't fit into its model? Probably a very naive question but this is entirely outside my domain.

oliwarner 8y ago

While your examples are accurate for hand-held or casual ANPR, they're easy to mitigate against when you're planning a fixed installation. Just look at existing ANPR installations. You get tight lanes. Barriers to arrest cars. High and low angle cameras working together. Heavy use of IR illumination (reads through all but the thickest mud IME). That's not to say some people don't get it wrong. I've worked on systems where they've cheaped-out and put a single camera at bumper-level. It's completely ineffective 2-4pm in winter because of the glare. But yeah, if you can avoid being a cheap idiot, you can easily take a system to 90+% confidence.

king_magic 8y ago
Right but it’s trading an image processing Rube Goldberg machine for a hardware Rube Goldberg machine.

24gttghh 8y ago

>Put that car at an angle and see how well that works. Or maybe add rain. Or bright sunshine. Or make it dark. Why would the car be at an angle? These type's of systems are usually directly overhead of a lane of traffic I would imagine; like at a tollbooth. The rest of that would be the job of the camera itself to best expose the image, no?