Using Raspberry Pi NoIR camera, I am trying to do occupancy detection using OpenCV for shared office space.
In order to identify vacant spots, I am using image of empty office space as a background model. Once we have the background model, I subtract the live video frame from the background image model(using cvAbsDiff) and identify the changes.
I am getting the images using a webcam mounted on the roof. The problem is to identify a person from this angle. When I work with live video, there are subtle variations (changes in chair position, physical objects or variations of camera) which act as noise. I tried using BackgroundSubtractorMOG, MOG2 but MOG and MOG2 can only do differences from a video stream but not using an image reference model.
Any inputs on possible directions will be greatly appreciated. I will be happy to share the technique with you guys that works once I have a working solution. Thanks!
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I wanted to capture a pair of stereo images with my iPhone camera. I wanted to know how could I do it. What ai have tried so far is, clicked one photo moved my phone a little to the right (almost 1.5 inches), but I did not get the desired results.
I want to rectify those images before I could apply opencv's StereoBM_create function to them. But I can't capture proper stereo images.
I've just gotten started learning about calculating depth from stereo images and before I went and committed to learning about this I wanted to check if it was a viable choice for a project I'm doing. I have a drone with a single rgb camera that has sensors that can give the orientation and movement of the drone. Would it be possible to sample two frames, the distance, and orientation differences between the samples and use this to calculate depth? I've seen in most examples the cameras are lined up horizontally. Is this necessary for stereo images or can I use any reasonable angle and distance between the two sampled images? Would it be feasible to do this in real time? My overall goal is to do some sort of monocular slam to have this drone navigate indoor areas. I know that ORB slam exists but I am mostly doing this for a learning experience and so would like to do things from scratch where possible.
Thank you.
I have a custom USB camera with a custom driver on a custom board Nvidia Jetson TX2 that is not detected through openpose examples. I access the data using GStreamer custom source. I currently pull frames into a CV mat, color convert them and feed into OpenPose on a per picture basis, it works fine but 30 - 40% slower than a comparable video stream from a plug and play camera. I would like to explore things like tracking that is available for streams since Im trying to maximize the fps. I believe the stream feed is superior due to better (continuous) use of the GPU.
In particular the speedup would come at confidence expense and would be addressed later. 1 frame goes through pose estimation and 3 - 4 subsequent frames are just tracking the object with decreasing confidence levels. I tried that on a plug and play camera and openpose example and the results were somewhat satisfactory.
The point where I stumbled is that I can put the video stream into CV VideoCapture but I do not know, however, how to provide the CV video capture to OpenPose for processing.
If there is a better way to do it, I am happy to try different things but the bottom line is that the custom camera stays (I know ;/). Solutions to the issue described or different ideas are welcome.
Things I already tried:
Lower resolution of the camera (the camera crops below certain res instead of binning so cant really go below 1920x1080, its a 40+ MegaPixel video camera by the way)
use CUDA to shrink the image before feeding it to OpenPose (the shrink + pose estimation time was virtually equivalent to the pose estimation on the original image)
since the camera view is static, check for changes between frames, crop the image down to the area that changed and run pose estimation on that section (10% speedup, high risk of missing something)
I am building a stereo vision system, but when I start my videoCapture with opencv, the frame is zoomed. Instead of showing the whole face with background for example, it only shows a big zoom to the chin.
It is USB camera bought on Amazon, just a PCB with lens and USB cable. I can only set the focus with a screw on the lens.
Any ideas how I can solve this?
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.