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+# Realtime Object Detection for RTSP Cameras
+- 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
+
+## Getting Started
+Build the container with
+```
+docker build -t realtime-od .
+```
+
+Download a model from the [zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
+
+Download the cooresponding label map from [here](https://github.com/tensorflow/models/tree/master/research/object_detection/data).
+
+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://localhost:5000
+
+## Future improvements
+- 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