It didn't detect face here: http://i.imgur.com/c34dX.jpg The algorithm probably got distracted and raised an exception.
Show HN: API for detecting people, cars, and everyday objects in images
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Re: Show HN: API for detecting people, cars, and everyday objects in images
#42It'd be great if you could use this to detect nudity. Any plans for that? I'm assuming the balls on the "in the works" list are of the sport variety? ;) In the works: Shoes Balls Smartphones and tablets Dogs Keyboards Cups and glasses Doors Keys
Re: Show HN: API for detecting people, cars, and everyday objects in images
#43Re: Show HN: API for detecting people, cars, and everyday objects in images
#44Re: Show HN: API for detecting people, cars, and everyday objects in images
#45Re: Show HN: API for detecting people, cars, and everyday objects in images
#46Earlier quoted context omitted.
The browser demo page says "Dextro supports up to 4 objects to be detected concurrently when used via the full API.", I think that's the problem.
Hey tunnuz and limejuice, sorry to hear we only picked up on 4 of the planes. We've biased our service towards precision rather than recall; thus, we try to be wrong about detected objects as little of the time as possible at the expense of perhaps missing a few object instances. I want to clarify: the 4 object concurrent detection refers to 4 classes of objects. On the Experiment page, you can only choose one class…
Re: Show HN: API for detecting people, cars, and everyday objects in images
#47As a computer vision researcher, I am not impressed by this. It is primarily an api for smartphone app makers who want a binary result for detection. It does not help with scene context analysis. For instance, if I have a big picture of a airplane on a wall, it will detect the airplane.. Does it know that this airplane is in the sky? or on a wall? There are a 1000 failure cases.
Re: Show HN: API for detecting people, cars, and everyday objects in images
#48Object detection is an extremely tough problem (some would say it is the computer vision problem ;-)), and while we've made a lot of progress in the past decade, the best methods are still terrible [2] -- average detection precision between 30-50%. For reference, most consumer applications require an AP of 90+% to be considered usable.
So if this is a completely automated solution, it's not going to be able to do much better, unless the creators can make massive (I mean orders-of-magnitude) improvements on the state-of-the-art.
But that being said, there are some applications where lower performance is acceptable. And if you add some manual verification, you could conceivably make this much better (with an increase in latency, though). Another possibility is to specialize on a certain type of input image (e.g., if you're a company taking photos in your warehouse, where all your photos look very similar and/or you can control the lighting and environment).
Still, I'm excited to see companies attempting to take object detection out to the real world. All the best to these guys!
[1] http://pascallin.ecs.soton.ac.uk/challenges/VOC/
[2] http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2011/resu...
Re: Show HN: API for detecting people, cars, and everyday objects in images
#49This is not Pedro Felzenszwalb's discriminative part-model algorithm. This is simple AdaBoost. The authors have labeled a bunch of datasets (1000s of them) and are able to detect whatever object. AdaBoost (Viola/Jones) is the most popular Yes/No detector, there is an OpenCV api for it. It used for detecting faces and license plates in commercial applications. Full person detector is nothing but a SVM+HOG descriptor.…