I'm trying to develop a solution that can track/recognize people with multiple cameras (raspberry pis) in a room. The idea is to track the path of one person in one/multiple rooms.
Solution would be to use raspberry pis, detects motion and send images to a backend solution which then implement an opensource solution or make use of multiple third party libraries.
For the moment we are only using Face recognition to recognize people in different images, but the solution is not good as it is only accurate with front face images and it is quite slow. Do you have other ideas on what can be done to improve results? Color tracking? Clothes recognition? Any third parties/opensource solution?
Thanks a lot
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I and my partner decided to implement a traffic light recognition program as a student project.
But we are absolute beginners with computer vision and have no idea how to start with this. (What only we know is to use OpenCV)
Should we firstly learn image recognition or just start with object tracking?
Our ideal production is to recognize traffic light in a video but not just an image.
In my opinion, you should take a serious course about computer vision before going deeper.
The video is just a sequence of picture. So you could use opencv to read each image then process them.
For you current project, a simple object detection using hog feature should be more than enough.
There's tutorial at http://www.hackevolve.com/create-your-own-object-detector/ . It's very easy to understand and source code is also available, so you can move quick.
Good luck.
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'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...)
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 want to build a video based tracking software. I can manage the control and display quite easily but the actual object tracking in a video stream is very difficult (color tracking is not an option).
Solutions like openCV would probably require a very long learning curve which I can't afford ATM.
Are there professional packages which expose a simple API for object tracking? C# and C++ are the preferred languages but other would be fine as well. Price is also less of an issue.
Computer Vision System Toolbox for MATLAB provides tracking functionality. Please check out the following examples:
Tracking a face
Tracking multiple objects
Generally, a lot depends on the specific problem you are trying to solve. Is the camera moving or stationary? Do you need to track a single object or multiple objects? Does your object have a distinctive color or texture? Does your object move in some predictable way?
Use OpenTLD. It tracks almost anything, but at a time, track only one thing. And code is in matlab.