Efficient module for automatic detection of people and vehicles using video surveillance cameras (VAPI)

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.

Tasks:

• 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.