Aim of the project: In cooperation with LTD “MONDOT”, develop a computationally efficient neural network-based module, which would be able to detect several classes of objects (people, cars, trucks, scooters, etc.) in surveillance videos.


  • Explore different novelties in the deep learning field to improve computationally efficient recurrent neural network-based object detection method, developed by EDI leading researcher Roberts Kadiķis (RNN-VDL).
  • Develop a specific data annotation tool to speed up the labeling process and create datasets for training deep networks.
  • Identify and train accurate and efficient neural network models and fine-tune their hyperparameters.
  • Implement the trained models on Raspberry Pi 4 computer, and demonstrate real-time object detection on video data.
Data annotation tool with GUI

Participating scientists

    Mg. sc. ing. Anatolijs Zencovs

    Research assistant

    Mg. math. Laura Leja


    +371 67558147
    [protected] Ivars Namatēvs


    +371 67558-129
    Dr. sc. ing. Roberts Kadiķis

    Senior Researcher

    +371 67558134