Multi action recognition using machine learning

Abstract

One of the most exciting and useful computer vision research topics is automated human activity identification. The majority of existing research, as well as most traditional approaches and classic neural networks, disregard the In video sequences, the appearance and patterns of motion are important. Individuals are unableIn video sequences, the appearance and patterns of motion are important.. This paper outlines a system for detecting, recognising, and summarising diverse human actions.The multiple action detection method takes the silhouettes of human bodies and then uses motion detection and tracking to create a unique sequence for each one.Based on the Each of the recovered sequences is then separated into shots based on the similarity between each pair of frames.show the sequence's homogenous activity. The activity is identified by looking at thedata oriented gradient (HOG) histogram of the frames in each shot's Temporal Difference Map (TDMap). comparing generated HOG to previous HOGs in the early stages of training. Making use of the TDMap image and a proposed CNN model, we can additionally recognise the action. Action summarising is carried out for each person found.