I'm a Software Engineering student in my last year in a 4-year bachelor degree program, I'm required to work on a graduation project of my own choice.
we are trying to find a way to notify the user of any thing the gets on his/her way while walking, this will be implemented as an android application so we have the ability to use the camera, we thought of Image processing and computer vision but neither me or any of my group members have any Image processing background, we searched a little bit and we found out about OpenCv.
So my question is do I need any special background to deal with OpenCv? and is it a good choice for the objective of my project to use computer vision, if not what alternatives do u advise me to use?
I appreciate your help.. thanks in advance!
At the first glance I would use 2 standard cameras to find depth image - stereo vision (similar to MS Kinect depth sensor)
from that it would be easy to fix a threshold to some distance.
Those algorithms are very CPU hungry so I do not think it will work on Android (although I have zero experience).
I you must use Android, I would look for some depth sensor (to avoid extracting depth data from 2 images)
For prototyping I would use MATLAB (or Octave), then I would switch to OpenCV (pointers, mem. allocations, blah...)
Related
I am working on identifying an object by using Kinect sensor so to get x,y,z coordinates of the object.
I am trying to find the related information for this but could not able to find much. I have seen the videos as well but nobody is sharing the information or any sample code?
This is what I want to achieve https://www.youtube.com/watch?v=nw3yix3XomY
Proabably, few people may asked same question but as I am new to the Kinect and these libraries due to which I need little more guidance.
I read somewhere that object detection is not possible using Kinect v1. We need to use 3rd party libraries like open CV or point-clouds (pcl).
Can somebody help me that even by using third party libraries how exactly can I identify object via a Kinect sensor?
It will be really helpful.
Thank you.
As the author of the video you linked stated in the comment, following this PCL tutorial will help you. As you found out already, realizing this may not be possible using the standalone SDK. Relying on PCL will help you not reinvent the wheel.
The idea there is to:
Downsample the cloud to have less data to deal with in the next steps (this also reduces noise a bit).
Identify keypoints/features (i.e. points, areas, textures that remain somehow invariant to some transformations).
Compute the keypoint descriptors, mathematical representations of these features.
For each scene keypoint descriptor, find nearest neighbor into the model keypoints descriptor cloud and add it to the correspondences vector.
Perform clustering on the keypoints and detect the model in the scene.
The software in the tutorial needs the user to manually feed in the model and scene files. It doesn't do that on live feed, as the video you linked.
The process should be pretty similar though. I'm not sure how cpu-intensive the detection is, so it might require additional performance tweaking.
Once you have frame-by-frame detection in place, you could start thinking about actually tracking an object across the frames. But that's another topic.
I would like to use computer vision to do the following:
A camera is mounted outside a building, capturing a videostream of the street below. The camera is installed approximately 5-6 meters above the street.
Whenever a person wearing a certain kind of hat(white, round) is captured by the camera, an event should be triggered.
Which algorithm should I look into to implement this kind of behavior ?
Is this best achieved through training the algorithm with sample data or is there another way to tell it to look for this type of hat ?
Also, how do I use multiple frames of video to increase the quality of detection ?
Edit: Added a picture of the hat
Before we do everything in comments I will start an answer here.
The first link you posted describes a simple color-based detection. You can try that, but it will fail if there are other pixel clusters of similar color in the image. Your idea of combining it with tracking is good: Identify clusters, build trajectories over several images, and only accept plausible trajectories as a hit. For robust tracking you may want to look into Kalman filtering. A problem you will most likely encounter is that a "white" hat will hardly be "white" in the images your camera delivers.
The second link you refer to - boosted Classifiers Based on Haar-like Features - is for detection of more complex objects. It probably won't help you find white blobs. Invest your time and energy in learning about tracking.
I'm happy to repeat myself here: "Solving a computer vision problem" is not something like "sorting an array". OpenCV is not the C++ Standard Library. You can use an std::map without knowing anything about a red-black tree. But (IMHO) you can't use Vision APIs without knowing a good deal of the math and theory. Working solutions Computer Vision are typically heavily tuned towards the specific problem scenario. Sorry if that sounds pedantic, but it explains why your question got beaten.
I'm planning on doing my Final Year Project of my degree on Augmented Reality. It will be using markers and there will also be interaction between virtual objects. (sort of a simulation).
Do you recommend using libraries like ARToolkit, NyARToolkit, osgART for such project since they come with all the functions for tracking, detection, calibration etc? Will there be much work left from the programmers point of view?
What do you think if I use OpenCV and do the marker detection, recognition, calibration and other steps from scratch? Will that be too hard to handle?
I don't know how familiar you are with image or video processing, but writing a tracker from scratch will be very time-consuming if want it to return reliable results. The effort also depends on which kind of markers you plan to use. Artoolkit e.g. compares the marker's content detected from the video stream to images you earlier defined as markers. Hence it tries to match images and returns a value of probability that a certain part of the video stream is a predefined marker. Depending on the threshold you are going to use and the lighting situation, markers are not always recognized correctly. Then there are other markers like datamatrix, qrcode, framemarkers (used by QCAR) that encode an id optically. So there is no image matching required, all necessary data can be retrieved from the video stream. Then there are more complex approaches like natural feature tracking, where you can use predefined images, given that they offer enough contrast and points of interest so they can be recognized later by the tracker.
So if you are more interested in the actual application or interaction than in understanding how trackers work, you should base your work on an existing library.
I suggest you to use OpenCV, you will find high quality algorithms and it is fast. They are continuously developing new methods so soon it will be possible to run it real-time in mobiles.
You can start with this tutorial here.
Mastering OpenCV with Practical Computer Vision Projects
I did the exact same thing and found Chapter 2 of this book immensely helpful. They provide source code for the marker tracking project and I've written a framemarker generator tool. There is still quite a lot to figure out in terms of OpenGL, camera calibration, projection matrices, markers and extending it, but it is a great foundation for the marker tracking portion.
So, let's say I have the longitude/latitude or address of many buildings
I can get satellite images, "street view", and perhaps 3d/perspective views of buildings.
I want to find the:
height,
number of floors,
floor area (max building footprint)
of the building. about 200k buildings.
Is there a library for recognizing buildings from satellite shots or pictures? Kind of like face detection I suppose.
Any other suggestions?
Thanks!
Yes and no. You will have to program many of the feature recognition yourself, but this should just be edge detection and point-of-interest detection (corners, straight lines, grids) to work out how many windows high a building is.
To do this you might want to checkout OpenCV (Computer Vision) which has C routines for real-time image analysis and common operations such as edge detection. I can also recommend DIP3e as a book to read and Image Processing in C and it's Java counterpart (if Java floats your boat).
Image processing is fun, and this sounds like a great project. There is tons on the Internet to learn from ... check out AI techniques to train image recognition systems to get better using a known sample set. Fun times!
Also, be careful of perspective ;)
good luck
I'm new to image processing and I want to do a project in object detection. So help me by suggesting a step-by-step procedure to this project. Thanx.
Object detection is a very complex problem that includes some real hardcore math and long tuning of parameters to the computation methods involved. Your best bet is to use some freely available library for that - Google will help.
There are lot of algorithms about the theme and no one is the best of all. It's usually a mixture of them what makes the best solution to the solution.
For example, for object movement detection you could look at frame differencing and misture of gaussians.
Also, it's very dependent of your application, the environment (i.e. noise, signal quality), the processing capacity you may have available, the allowable error margin...
Besides, for it to work, most of time it's first necessary to do some kind of image processing to the input data like median filter, sobel filter, contrast enhancement and a large so on.
I think you should start reading all you can: books, google and, very important, a lot of papers about the subjects (there are many free in internet) you are interested in.
And first of all, i think it's fundamental (at least it has been for me) having a good library for testing. The one i have used/use is OpenCV. It's very complete, implement many of the actual more advanced algorithms, is very active, has a big community and it's free.
Open Computer Vision Library (OpenCV)
Have luck ;)
Take a look at AForge.NET. It's nowhere near Project Natal's levels of accuracy or usefulness, but it does give you the tools to learn the algorithms easily. It's an image processing and AI library and there are several tutorials on colored object tracking and motion detection.
Another one to look at is OpenCV from Intel. I believe it's a bit more advanced, but it's written in C.
Take a look at this. It might get you started in this complex field. The algorithm pages that it links to are interesting reading.
http://sun-valley.stanford.edu/projects/helicopters/final.html
This lecture by Jeff Hawkins, will give you an idea about the state of the art in this super-difficult field.
Seems that video disappeared... but this vid should cover similar ground.