I am using AiTool with DeepStack and I have a few suggestions that worked for me to improve object identification in DeepStack. First some information about my hardware. The BI PC is an I7-6700 with 32GB RAM. My cameras are
IPC-T2431T-AS 3.6mm from EmpireTech, 4 in total. Below are my camera settings.
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I am running 5 instances of the CPU version of DeepStack in a Docker container. Windows is configured for WSL2 for the Docker container. DeepStack MODE=High with processing times below.
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Below is a typical resource usage without any motion. Docker is the VMMEM process and it is using most of the resources. The jpegs are sent to a 3GB RAM Disk and that is part of the 37% memory usage below. With all 4 cameras triggered the CPU usage gets up to 70%.
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I have BI configured using both the main and sub stream. A cloned camera is detecting motion and generating the jpeg. I trigger the Clone Master with my Home Automation via url for event recording. The clone is using the sub stream to detect motion but generating a jpeg with the main stream. You accomplish jpeg generation using the main stream by enabling "Pre-trigger video buffer" I set the buffer to 2 seconds but I could lower that closer to 1 second. The jpegs are generated every 3 seconds with a break time of 6 seconds under the trigger settings so 3 images are taken and sent to DeepStack for each event. During daylight I have the image quality of the jpeg set to 50%. My minimum confidence is set at 42% for a person. In the daytime persons are identified with 70%-80% confidence. Occasionally my dog will be identified as a person in the daytime with a confidence as high as 40% which is why the confidence level for the person is at 42%.
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After dark the BI profile changes and I boost the jpeg quality to 100%. The images at night are black and white so the jpeg size is actually smaller and processes about the same at night. The Home Automation system is set differently at night in that a push notification and text will not be sent out unless a person has been identified twice by any combination of cameras within 5 minutes. This is because DeepStack will identify the dog as a person with a high confidence level at night but very rarely twice in the 5 minute interval. Using the main stream for jpeg generation, increasing the image quality at night and running DeepStack in MODE=High improved my results.