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35 lines
1.5 KiB
Markdown
35 lines
1.5 KiB
Markdown
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# Realtime Object Detection for RTSP Cameras
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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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## Getting Started
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Build the container with
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```
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docker build -t realtime-od .
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```
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Download a model from the [zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
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Download the cooresponding label map from [here](https://github.com/tensorflow/models/tree/master/research/object_detection/data).
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Run the container with
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```
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docker run -it --rm \
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-v <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro \
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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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realtime-od:latest
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```
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Access the mjpeg stream at http://localhost:5000
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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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