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How to create a real time image stitcher?
In order to (1) create a real-time image stitcher and (2) perform motion detection on the panorama image, we’ll assume that both cameras are fixed and non-moving, like in Figure 1 above. Why is the fixed and non-moving assumption so important?
Why is stitching image faster than image rendering?
One option of our developed software is stitching the images without recomputing the keypoints and homography at every frame. In such cases, image rendering will become the bottleneck of the pipeline. With faster image rendering libraries, we could further improve the video FPS.
What’s the fastest way to stitch a video?
The higher the resolution, the more computationally heavy it is to stitch the videos. We will use the average execution time of each frame to calculate speedup, and use the FPS as a measure of throughput. As for testing platform, we will use the AWS EC2 t2.2xlarge instance for baseline and shared memory parallel program.
How to do real time stitching in Python?
Finally, the realtime_stitching.py file is our main Python driver script that will access the multiple video streams (in an efficient, threaded manner of course), stitch the frames together, and then perform motion detection on the panorama image.
How to stitch a panorama image in OpenCV?
Access multiple camera streams at once. Apply image stitching and panorama construction to the frames from these video streams. Perform motion detection in the panorama image. Again, the benefit of performing motion detection in the panorama image versus two separate frames is that we won’t have any “blind spots” in our field of view.
Why do you need to stitch video from multiple cameras?
Due to sensor resolution and optics limitations, the field of view (FOV) of most cameras is too narrow for applications such as autonomous driving and virtual reality. A common solu- tion is to stitch the outputs of multiple cameras into a panoramic video, effectively extending the FOV.
How is stitching solved for linear camera arrays?
When the optical centers of these cameras are nearly co-located, stitching can be solved with a simple homography transformation.