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Can anyone direct me to a free people tracking library? I would like to be able to use an input image (or video) and get the position of people in it. I have looked at the Reading People Tracker but cannot compile it! I am on Linux (Ubuntu) but windows would be okay (if anyone can tell me how, this would be great). Preferably, it would be for C/C++ but java, c#, ruby and python would be okay too. Thanks in advance, ell.
Not specifically a people tracking library, but as a general tracking approach "Predator" is very highly regarded: http://info.ee.surrey.ac.uk/Personal/Z.Kalal/tld.html - Authors site includes some very impressive demonstration of results on youtube
This article fully explains an algorithm used for tracking moving people, and the accompanying source code is here; it is pure Java. You can see it in action in this video.
(Disclaimer: I'm the author; but I do think this is very useful, and have successfully used the algorithm a lot myself.)
The algorithm tracks moving objects in general, finds their bounding rectangle (which the application draws), counts the number of pixels in each objects, and consistently assigns them the same integer ID throughout the video frames.
When it comes to commercial computer vision applications, OpenCV and the Point Cloud Library aka PCL are your best friends (C++, but there are Java and C# bindings). And articles like the one linked explains how to use tools like OpenCV to accomplish full stack motion tracking. (The pure Java implementation shows how it works down to the individual pixels.)
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I have a problem related to computer vision and machine learning. Basically we are working on video surveillance system which will be trained to detect any suspicious activity like theft or shop lifting in stores.We are confused that is that will be able to solve this problem or not. We don't know that is it feasible or not. So kindly just suggest us something. Any help will be appreciated.
While I understand that Open CV is great for face-detection and usable for face-recognition, can it be used for analyzing "actions", s.a. the act of sitting, the act of lifting an object off the shelf ? If so, what are some of these algorithms I should dig deeper into ?
Are there other libraries (other than OpenCV) which need to be used for such tasks? Are there open-source libraries for the same?
What you are trying to achieve is currently a very active area in computer vision and machine learning research called Behaviour Analysis or Activity Detection. State of the art approaches can be found in journals like PAMI or conferences like CVPR or NIPS. As of today, it is nowhere near the performance you would require to build an automatic theft-detection system in the general case (i.e., any surveillance camera looking into any scene in any orientation). Behaviour Analysis is based on many underlying techniques, such as identifying the pose of people in images. Current research is still trying to figure out if there's a person in the picture and the position of its limbs in the general case.
Here's what might be feasible with the current research state: A system that help an operator focus on potential threats when cameras have a clear unobstructed view to a clear and mostly static environment (e.g., glass displays). An operator could therefore monitor many more cameras than before, because the system will automatically hide the cameras that clearly does not contain suspicious activity or movement.
To know more about current possibilities, I recommend you to check the literature (like this example), decompose the problem into subparts and leverage your priors (your a priori knowledge of the scene and people you're looking at) as much as possible.
By using object recognition (by helping deep learning) we can detect object and by using the data set of recorded object in the shop we can assess to the detailed (price) of that object. based on the number of objects and information about the object we can recognize the issue such as thrift in the counter.
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I'm trying to learn how to build an augmented reality app to place a 3D object into the world without the need for a "target". So far, pretty much every augmented reality framework seems to only work with targets; Vuforia, Wikitude, etc. Some have slight extensions on target tracking, such as Vuforia's "Extended Tracking" and "Smart Terrain" features, but in the end these are afflicted by the same limitation -- the SDKs are hopelessly obsessed with "targets".
Then I came across Google Tango. It's hard to tell exactly, but it appears to be the only option I could find which offers placing 3D objects into the real world and allowing the user to walk around with the 3D objects staying in place (relative to the world).
Am I correct in my assumption that Google Tango is the only option for this? If I'm going to spend $512 on a Tango development kit, I want to first make sure there weren't other augmented reality libraries I could have used for this.
Kudan provides markerless tracking, via SLAM. They have a free trial version and no hardware necessary.
Markerles tracking is not new. 13th Lab's PointCloud SDK prodided markerless tracking a few years ago. But it removed that offering when their implementation was licensed exclusively by a 3rd party.
The SLAM algorithm is neither new nor proprietary. Anyone can implement it from the CalTech academic paper, though it's a graduate-level problem to tackle!
Check out http://easyar.com markerless tracking and other cool features. And it is free and opensource
Wikitude also have a markerless tech. I have not tried it to tell how good it works.
Vuforia had something called Smart Terrain which was more of an interaction with environment. But the tech is quite old and I am not sure they kept it going in the last couple of years.
Actually, none can really claim to have a working markerless, it is more of a working in certain conditions. You need proper hardware fast enough to process all the calculations and limited usage conditions as no fast movement and ideal environment.
Microsoft is also coming up with Hololens but hardware is at $3000 and only for dev at the moment.
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I am iOS developer. I am new with doing object tracking feature. I have searched many links based on tracking real objects. But I have found many links like image tracking, image matching and all. Likewise I would like to track the real world objects.And also I have gone through a site below:
http://developers.arlab.com/me
It helps a lot for tracking images, image matching etc. But not specified any object tracking. If anybody suggesting good tutorial or having any sample source code of object tracking, please share.
Advance Thanks for your support.
You can use the OpenCV framework for Object Tracking. There are many nice tutorial and documentation on internet.
Below i have listed some.Please check if this helps you
Official Site
OpenCV is released under a BSD license and hence it’s free for both academic and commercial use. It has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. OpenCV was designed for computational efficiency and with a strong focus on real-time applications...
http://opencv.org/
You can download the SDK from here
Demo Github project
https://github.com/Itseez/opencv
https://github.com/atduskgreg/opencv-processing
These are some examples you can find many more.
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I am very beginner in robotics. I want to make a robotics project based on slam algorithms. I know many algorithm and i have the confidence to implement it in any language but i dont have any idea based on image processing and hardware. So, can anyone give a tuotorial based on slam based robotics projects[including how hardware organized and how image processing is done for that project], after seeing that i can make a slam based robotics project from my own.
In addition, If anyone give me a video lecture series for that then it would be very helpful.
Thanks in advance.
I have tried to do something similar last year. I created two systems. The first system made use of a camera and laser to detect objects and determine their location relative to the system itself. The second system was a little robot with tracks (wheels would be better), that used dead reckoning to keep track of its own location relative to its starting location. The techniques worked really well, but unfortunately I did not have the time to combine the two systems. I can however provide you with some documentation that was incredibly useful for me at that time.
These tutorials provide information on both the hardware and the software.
Optical Triangulation (detection of objects with a camera and laser) :
http://www.seattlerobotics.org/encoder/200110/vision.htm
Dead Reckoning (a technique to keep track of one's own location) :
http://www.seattlerobotics.org/encoder/200010/dead_reckoning_article.html
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Are there any libraries, in any language, out there to help identify and grab the images of people in a still photo? Something similar in effect to the way the Kinect can isolate users.
Thanks much!
I think it depends very much on the setup (e.g. simple bg. with decent lighting condition vs. random bg. with random lighting). If you can make life easier for yourself and isolate a few simpler use cases that would be great. Still there are other available method, look at the plethora of research around pedestrian detection for example.
One thing I did try and it works surprisingly well although computationally intensive is the Histogram of Gradient Orientations, implemented in OpenCV as the HoG descriptor. For a still photo this should produce decent results. You can have a look at the OpenCV sample. I also recommend having a look at Dramanan's excellent papers.
Long story short, thanks for years of inspiring research in computer vision, there are quite a few interesting options out there, it's up to how willing you are to go into detail. Still, regardless of how clever algorithms can be, I believe it's far more important to get a decent setup that allows simple and efficient solutions rather than complex solutions that try to cater for every possible situation. Goodluck!