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
- Allows you to define specific regions (squares) in the image to look for motion/objects
- Motion detection runs in a separate process per region and signals to object detection to avoid wasting CPU cycles to look for objects when there is no motion
- Object detection with Tensorflow runs in a separate process per region 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
- Publishes motion and person detection scores to MQTT
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 --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>' \
-e REGIONS='<box_size_1>,<x_offset_1>,<y_offset_1>,<min_person_size_1>,<min_motion_size_1>,<mask_file_1>:<box_size_2>,<x_offset_2>,<y_offset_2>,<min_person_size_2>,<min_motion_size_2>,<mask_file_2>' \
-e MQTT_HOST='your.mqtthost.com' \
-e MQTT_MOTION_TOPIC='cameras/1/motion' \
-e MQTT_OBJECT_TOPIC='cameras/1/objects' \
-e MQTT_OBJECT_CLASSES='person,car,truck' \
realtime-od:latest
Access the mjpeg stream at https://localhost:5000
- Lower the framerate of the RTSP feed on the camera to what you want to reduce the CPU usage for capturing the feed
- Use SSDLite models
- Switch to MQTT prefix
- Add last will and availability for MQTT
- Build tensorflow from source for CPU optimizations
- Add ability to turn detection on and off via MQTT
- MQTT reconnect if disconnected (and resend availability message)
- MQTT motion occasionally gets stuck ON
- Output movie clips of people for notifications, etc.
- Integrate with homeassistant push camera
- Store highest scoring person frame from most recent event
- Add a max size for motion and objects (height/width > 1.5, total area > 1500 and < 100,000)
- Make motion less sensitive to rain
- Use Events or Conditions to signal between threads rather than polling a value
- Implement a debug option to save images with detected objects
- Only report if x% of the recent frames have a person to avoid single frame false positives (maybe take an average of the person scores in the past x frames?)
- Filter out detected objects that are not the right size
- Make RTSP resilient to network drop outs
- Merge bounding boxes that span multiple regions
- Switch to a config file
- Allow motion regions to be different than object detection regions
- Implement mode to save labeled objects for training
- Add motion detection masking
- Change color of bounding box if motion detected
- Look for a subset of object types
- Try and reduce CPU usage by simplifying the tensorflow model to just include the objects we care about
- MQTT messages when detected objects change
- Implement basic motion detection with opencv and only look for objects in the regions with detected motion
- Dynamic changes to processing speed, ie. only process 1FPS unless motion detected
- Parallel processing to increase FPS
- Look into GPU accelerated decoding of RTSP stream
- Send video over a socket and use JSMPEG
https://www.tensorflow.org/install/source#docker_linux_builds
used tensorflow/tensorflow:1.12.0-devel-py3
docker run -it -v ${PWD}:/lab -v ${PWD}/../back_camera_model/models/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb:/frozen_inference_graph.pb:ro tensorflow/tensorflow:1.12.0-devel-py3 bash
bazel build tensorflow/tools/graph_transforms:transform_graph
bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
--in_graph=/frozen_inference_graph.pb \
--out_graph=/lab/optimized_inception_graph.pb \
--inputs='image_tensor' \
--outputs='num_detections,detection_scores,detection_boxes,detection_classes' \
--transforms='
strip_unused_nodes(type=float, shape="1,300,300,3")
remove_nodes(op=Identity, op=CheckNumerics)
fold_constants(ignore_errors=true)
fold_batch_norms
fold_old_batch_norms'