update readme

README.md

@@ -1,10 +1,12 @@
 # Realtime Object Detection for RTSP Cameras
+This results in a MJPEG stream with objects identified that has a lower latency than directly viewing the RTSP feed with VLC.
 - 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
+- A process is created per detection region
 
 ## Getting Started
 Build the container with
@@ -23,13 +25,18 @@ docker run -it --rm \
 -v <path_to_labelmap.pbtext>:/label_map.pbtext:ro \
 -p 5000:5000 \
 -e RTSP_URL='<rtsp_url>' \
+-e REGIONS='<box_size_1>,<x_offset_1>,<y_offset_1>:<box_size_2>,<x_offset_2>,<y_offset_2>' \
 realtime-od:latest
 ```
 
 Access the mjpeg stream at http://localhost:5000
 
+## Tips
+- Lower the framerate of the RTSP feed on the camera to what you want to reduce the CPU usage for capturing the feed
+
 ## 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
+- Parallel processing to increase FPS
+- Look into GPU accelerated decoding of RTSP stream
+- Send video over a socket and use JSMPEG