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@@ -1,10 +1,12 @@
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# Realtime Object Detection for RTSP Cameras
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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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- 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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## Getting Started
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Build the container with
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@@ -23,13 +25,18 @@ docker run -it --rm \
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-v <path_to_labelmap.pbtext>:/label_map.pbtext:ro \
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-p 5000:5000 \
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-e RTSP_URL='<rtsp_url>' \
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+-e REGIONS='<box_size_1>,<x_offset_1>,<y_offset_1>:<box_size_2>,<x_offset_2>,<y_offset_2>' \
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realtime-od:latest
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```
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Access the mjpeg stream at http://localhost:5000
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+## Tips
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+- Lower the framerate of the RTSP feed on the camera to what you want to reduce the CPU usage for capturing the feed
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+
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## Future improvements
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- MQTT messages when detected objects change
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- Dynamic changes to processing speed, ie. only process 1FPS unless motion detected
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-- Break incoming frame into multiple smaller images and run detection in parallel for lower latency (rather than input a lower resolution)
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-- Parallel processing to increase FPS
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+- Parallel processing to increase FPS
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+- Look into GPU accelerated decoding of RTSP stream
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+- Send video over a socket and use JSMPEG
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blakeblackshear