I am using DeepStackAi with Blue Iris (a video security software).
I have configured it with a test camera.
The camera I use is one that has quiet good night vision (IPC-HDW5231R-Z).
I am only identifying persons in the generated pictures (-e VISION-DETECTION=True in the docker command). I am using the CPU version.
Day time detection seems to work as it should. I did not get any false alerts during my testing.
Nighttime is very different - it doesn’t detect any persons at all.
I am pretty sure all is configured OK since day time works without any issues but not sure why it doesn’t detect at night.
I can try and tweak the night time picture - more contrast and such.
But I am wondering if there is a better way? Like to “train” the model by feeding it some of the nighttime pictured generated by Blue Iris?
Hello @MnM, thanks for bringing this up.
You can run DeepStack in high accuracy model by adding -e MODE=HIgh . This would run slower but would detect better.
Also, can you share sample images from the test camera, so we can investigate this better?
@john are there object detection models trained on images from night vision (IR cameras like the IPC-HDW5231R-Z)? I am also interested in true thermal imaging (FLIR) cameras
I expect the pictures that @MnM are not of such high resolution and contrast as these example images, but it is very interesting that a silhouette works so well! Suggests that some pre-processing of the IR images might improve accuracy, perhaps by applying thresholding?
How did you manage to tune Deepstack in order to have good nightime image detection? I have False Negatives always with IR b/w images and I have a good IR camera.
During daytime works really great.
If the illumination is insufficient, then no one and nothing will be able to recognize objects. Therefore, use photosensitive cameras. Or add light. I am using a DS-2CD2086G2-IU camera, she herself can recognize vehicles or people, and does this even in low light conditions, when the deep stack no longer sees. But she also recognizes large dogs as people.