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@@ -2,11 +2,12 @@
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This results in a MJPEG stream with objects identified that has a lower latency than directly viewing the RTSP feed with VLC.
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- Prioritizes realtime processing over frames per second. Dropping frames is fine.
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- OpenCV runs in a separate process so it can grab frames as quickly as possible to ensure there aren't old frames in the buffer
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-- Object detection with Tensorflow runs in a separate process and ignores frames that are more than 0.5 seconds old
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+- Motion detection runs in a separate process per region and signals to object detection to avoid wasting CPU cycles to look for objects when there is no motion
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+- Object detection with Tensorflow runs in a separate process per region and ignores frames that are more than 0.5 seconds old
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- Uses shared memory arrays for handing frames between processes
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- Provides a url for viewing the video feed at a hard coded ~5FPS as an mjpeg stream
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- Frames are only encoded into mjpeg stream when it is being viewed
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-- A process is created per detection region
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+- Publishes motion and person detection scores to MQTT
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## Getting Started
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Build the container with
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@@ -52,7 +53,7 @@ Access the mjpeg stream at http://localhost:5000
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- [x] Use Events or Conditions to signal between threads rather than polling a value
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- [x] Implement a debug option to save images with detected objects
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- [x] Only report if x% of the recent frames have a person to avoid single frame false positives (maybe take an average of the person scores in the past x frames?)
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-- [ ] Filter out detected objects that are not the right size
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+- [x] Filter out detected objects that are not the right size
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- [ ] Make resilient to network drop outs
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- [ ] Merge bounding boxes that span multiple regions
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- [ ] Switch to a config file
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blakeblackshear