• Prioritizes realtime processing over frames per second. Dropping frames is fine.
• 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
• Object detection with Tensorflow runs in a separate process and ignores frames that are more than 0.5 seconds old
• Uses shared memory arrays for handing frames between processes
• Provides a url for viewing the video feed at a hard coded ~5FPS as an mjpeg stream
• Frames are only encoded into mjpeg stream when it is being viewed

Build the container with

docker build -t realtime-od .

Download a model from the zoo.

Download the cooresponding label map from here.

Run the container with

docker run -it --rm \
-v <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro \
-v <path_to_labelmap.pbtext>:/label_map.pbtext:ro \
-p 5000:5000 \
-e RTSP_URL='<rtsp_url>' \
realtime-od:latest

Access the mjpeg stream at http:https://localhost:5000

• MQTT messages when detected objects change
• Dynamic changes to processing speed, ie. only process 1FPS unless motion detected
• Break incoming frame into multiple smaller images and run detection in parallel for lower latency (rather than input a lower resolution)
• Parallel processing to increase FPS