How to recognize rain on camera vision using with OpenCV in C++?
Or if somebody stick a sticker on a camera how recognize it with OpenCV in C++?
Or if somebody throw color to the camera how can i detect it with OpenCV in C++?
Detect these on camera vision:
Rain
Sticker
Color
Here is an example video of sticker!
Camera Vision-Sticker
In case of a sticker, you're just looking for a large dark area that doesn't change in time.
In case of color, analyze image color stats - if somebody sprays some paint on a camera (is that what you mean by "throwing color"?), some color is going to be dominant over all the others.
You can also try to handle both cases by subtracting frames and detecting image areas that don't change in time that way.
You may want to use machine learning for finding threshold values (e.g. area size, its shape properties, such as width/length ratio, continuousness etc.) used to decide when to consider something to be a sticker/color or something else.
As for the rain, I guess there's no simple answer that can be given in a few sentences. There are some articles available in the web though. That said, I would guess it would be simpler and cheaper to detect rain by just installing external rain sensors (like the ones activating wipers in a car) rather than trying to do it by developing your own computer vision algorithm for that purpose.
This sounds like an interesting project, where a camera can automatically detect obstruction (paint, sticker, rain). It will most likely be necessary for the camera to be mounted without obstructions so that the expected image can be learned. If the usage scenario allows that, it won't be very hard.Both sticker and rain result in strong permanent deviations from the expected image, while rain will result in noisy images.
OpenCV with C++ or Python can help solve this kind of problems, because complicated computer vision algorithms are already implemented there. It takes some time to get started with, but after that OpenCV is not hard.
Related
I am doing a project on face recognition, for that I have already used different methods like eigenface, fisherface, LBP histograms and surf. But these methods are not giving me an accurate result. Surf gives good matches for exact same images, but I need to match one image with it's own different poses(wearing glasses,side pose,if somebody is covering his face) etc. LBP compares histogram of images, i.e., only color informations. So when there is high variation on lighting condition it is not showing good results. So I heard about neural networks, but I don't know much about that. Is it possible to train the system very accurately by using neural networks. If possible how can we do that?
According to this OpenCV page, there does seem to be some support for machine learning. That being said, the support does seem to be a bit limited.
What you could do, would be to:
User OpenCV to extract the face of the person.
Change the image to grey scale.
Try to manipulate so that the face is always the same size.
All the above should be doable with OpenCV itself (could be wrong, haven't messed with OpenCV in a while) so that should save you some time.
Next, you take the image, as a bitmap maybe, and feed the bitmap as a vector to the neural network. Alternatively, as #MatthiasB recommended, you could feed the features instead of individual pixels. This would simplify the data being passed, thus making the network easier to train.
As for training, you manipulate these images as above, and then feed them to the network. If a person uses glasses occasionally, you could have cases of the same person with and without glasses, etc.
I'm new to OpenCV and computer vision stuff. We are having a robot project with ROS and Kinect. We want to evaluate whether the room has adequate lighting using Kinect. Is there a way to use OpenCV to process the Kinect camera information and evaluate the environment?
Thanks in advance.
OpenCV has methods for connecting up with the Kinect, so yes, you would be able to pull the Kinect RGB image from the device.
As for determining your lighting conditions, I believe the Kinect has an auto-gain function built into it. In a very dark environment, that auto gain is going to cause a large amount of noise. So if you do some experiments in dark and light environments, measure the noise in the imagery you might be able to tell if the image (and consequently environment) is too dark from the image noise.
You could look for differences in two images, one where you shine a light, and one where you don't. I imagine that the change will be minimal in a bright environment, but there will be big difference in a dark one.
You'd have to elaborate on what would be "adequate lighting" for this to be more than a binary result.
I'm trying to build a simple traffic motion monitor to estimate average speed of moving vehicles, and I'm looking for guidance on how to do so using an open source package like OpenCV or others that you might recommend for this purpose. Any good resources that are particularly good for this problem?
The setup I'm hoping for is to install a webcam on a high-rise building next to the road in question, and point the camera down onto moving traffic. Camera altitude would be anywhere between 20 ft and 100ft, and the building would be anywhere between 20ft and 500ft away from the road.
Thanks for your input!
Generally speaking, you need a way to detect cars so you can get their 2D coordinates in the video frame. You might want to use a tracker to speed up the process and take advantage of the predictable motion of the vehicles. You, also, need a way to calibrate the camera so you can translate the 2D coordinates in the image to depth information so you can approximate speed.
So as a first step, look at detectors such as deformable parts model DPM, and tracking by detection methods. You'll probably need to port some code from Matlab (and if you do, please make it available :-) ). If that's too slow, maybe do some segmentation of foreground blobs, and track the colour histogram or HOG descriptors using a Particle Filter or a Kalman Filter to predict motion.
Are there any open source code which will take a video taken indoors (from a smart phone for example of a home or office buildings, hallways) and superimpose that on a 2D picture showing the path traveled? This can be a handr drawn picture or a photo of a floor layout.
First I thought of doing this using the accelerometer and compass sensors but thought that perhaps one can get better accuracy with the visual odometer approach. I only need 0.5 to 1 meter accuracy. The phone will also collect important information indoors (no gps) for superimposing that data on the path traveled (this is the real application of this project and we know how to do this part). The post processing of the video can be done later on a stand alone computer so speed and cpu power is not a issue.
Challenges -
The user will simply hand carry the smart phone so the video taker is moving (walking) and not fixed
limit the video rate to keep the file size small (5 frames/sec? is that ok?). Typically need perhaps a full hour of video
Will using inputs from the phone sensors help the visual approach?
any help or guidance is appreciated Thanks
I have worked in the area for quite some time. There are three points which I'd care to make.
Vision only is hard
Vision based navigation using just a cellphone camera is very difficult. Most of the literature with great results show ~1% distance traveled as state-of-the-art but is usually using stereo cameras. Stereo helps a great deal, particularly in indoor environments for coping with scale drift. I've worked on a system which achieves 0.5% distance traveled for stereo but only roughly 5% distance traveled for monocular. While I can't share code, much of our system was inspired by this Sibley and Mei paper.
Stereo code in our case ran at full 60fps on a desktop. Provided you can push data fast enough, it'll be fine. With your error envelope, you can only navigate for 100m or so. Is that enough?
Multi-sensor is way to go. Though other sensors are worse than vision by themselves.
I've heard some good work with accelerometers mounted on the foot to do ZUPT (zero velocity updates) when the foot is briefly motionless on the ground while taking a step in order to zero out drift. This approach has the clear drawback of needing to mount the device on your foot, making a vision approach largely useless.
Compass is interesting but will be distracted by the ton of metal within an office building. Translating few feet around a large metal cabinet might cause 50+ degrees of directional jump.
Ultimately, a combination of sensors is likely to be the best if you can make that work.
Can you solve a simpler problem?
How much control do you have over your environment? Can you slap down fiducial markers? Can you do wifi triangulation? Does it need to be an initial exploration? If you can go through the environment before hand and produce visual bubbles (akin to Google Street View) to match against, you'll be much more accurate.
I'm not aware of any software that does this directly (though it might exist) but stuff similar to what you want to do has been done. A few pointers:
Google for "Vision based robot localization" the problem you state is very similar to the problem robots with a camera have when they enter a new environment. In this field the approach is usually to have the robot map its environment and then use the model for later reference, but the techniques are similar to what you'll need.
Optical flow will roughly tell you in what direction the camera is moving, but it won't tell you the speed because you have no objective reference. This is because you don't know if the things you see moving in the video feed are 1cm away and very small or 1 mile away and very big.
If you know the camera matrix of the camera recording the images you could try partial 3D scene reconstruction techniques to take a stab at the speed. Note that you can do the 3D scene stuff without the camera matrix (this is the "uncalibrated" part you see in the title of a lot of the google results), the camera matrix will let you add real world object sizes (and hence distances) to your reconstruction.
The amount of images/second you need depends on the speed of the camera. More is better, but my guess is that 5/second should be sufficient at walking speeds.
Using extra sensors will help. Probably the robot localization articles talk about this as well.
Is there a very basic color/shape detection mechanism through which one could detect a specific color or a shape in a webcam feed? Wanted to use the color/or shape as a symbolic marker for an AR application.
Though the ideal case would be a NFT , but i am not much of coder and have no experience in OpenCV( have read a lot about it in previous discussions here).Have worked so far with the SLAR tooolkit only and that offers only the basic b/w marker detection
And the more easily useable NFT libraries are , well, not freeware :/
Any guidance to integrate the abovementioned detection routines in a .Net/Flash environment would be of great help.
Color detection is very easy: take your videostream images, convert them to binary images by using the RGB value as a vector (like RGB = [0,255,0] = green), and setting other vectors within a given distance as positive hits. This is one of the easiest forms of computer vision, and a couple of early CV-based PS2 games involved detecting brightly colored props.
This is my favorite paper on shape recognition - if you want to detect simple 2D outlines on flat surfaces, this is a great technique.
I'm neither a .Net or a Flash programmer, so I can't offer any help there.