Skip to content

Latest commit

 

History

History
58 lines (55 loc) · 3.43 KB

ROADMAP.md

File metadata and controls

58 lines (55 loc) · 3.43 KB
Raw

  • Main functionality:

    • Read video
    • Initialize neural network
    • Extract bounding boxes of detected objects
    • Filter detected objects by classname and confidence
    • Do non maximum suppression for additional filtering
    • Put bounding boxes and classnames onto image after NMS
    • Display an output
    • Redis (pub to user defined channel + consider password usage)
    • Implement SORT + Deep SORT [I guess we need ndarray, not nalgebra] W.I.P.

  • Additional functionality

    • Usage of custom implementation (via nalgebra) of Kalman filter
    • Tracking via custom implementation of Kalman filter
    • Usage of OpenCV-based Kalman filter Not needed
    • Tracking via OpenCV-based Kalman filter Not needed
    • Spatial converter
      • Tranform matrix
      • Convert function
    • Speed evaluations
      • Haversine function
      • Spatial converter usage
      • Apply math function to objects
    • Read frames in one thread and do neural network's job in another one
    • Proper logging?
    • Error handling W.I.P.
    • MJPEG streamer (via actix-web I guess?) [it's implemented via mspc and tokio, but I'm not sure if I do threads stuff correctly. MJPEG streaming are laggy currently, need to investigate]
    • gRPC for clients (do we need this?) [I guess it should work as redis publisher, so only client will be implemented?]
    • REST JSON for clients Still ugly
    • REST Websockets for clients (do we need this?. No)
    • Installation instructions (Makefile+Ubuntu18-20)
    • TOML configuration
    • Convex polygons math
      • Check if point is in polygon
      • Check if point has entered into polygon
      • Check if point has left polygon
    • Hashmap and timer for estimating average values of traffic (speed, intensity)
      • ~~ Hashmap and thread ~~
      • ~~ Reset values ~~
      • ~~ Time intervals (threads) ~~
      • Refactor
    • Optional choice between Tiny YOLO and MobilenetSSD(Caffe)[removed]
    • Fill Jetson Nano instructions
    • Estimate speed and intensity for each vehicle type
    • RabbitMQ/NATS/Tarantool/Kafka integration module? Do we need those?
    • Verbose output as option (fps/workers output)
    • Consider color as hex representation (additional field with priority lower than RGBA)
    • Consider training YOLO v4 tiny for vehicle classes only (we do need false positive detection for peoples/boats/chairs and etc.) [I want to provide link for custom trained network: only vehicles + network size is 416x256 rather than 416x416 due the problem when resizing image provided too much squeezing]
    • Organize structure to reduce connectiviy of components of applications
    • Consider YOLOX (nano/tiny) [Well, I checked it and it not that good as I was thought]
    • ~~More neural network parameters in TOML ~~
    • Figure it out, how to boost perfomance for YOLOv4-tiny (or mobilenet-ssd?). Best idea I have so far is: pick tensort-rt and onnx and do magick trick. [But there are no good and mature tensor-rt libs for Rust I guess...]
    • Scaling by x/y
    • Swagger docs.

  • Some bugs

    • RAM consumption grows up too much.