Implementation of Video-Based Human Anomalous Activity Detection Using LSTM-RNN Network
S L Prathapareddy, Neelam Sharma, V. Jayalakshmi, D. Suganthi, S. Sathiya Naveena, Rajanish Kumar Kaushal · 2023
Increased interest in video analysis technology, and in particular automatic human activity recognition, has arisen in response to the rising requirements of a wide range of applications, including surveillance, entertainment, and healthcare systems. Using automated reporting, authorities might be alerted to the presence of a possible criminal or dangerous actor, such as a person hanging about an airport or railway station with a bag. Similarly, activity identification can improve human-computer interaction in the gaming industry by, say, automatically identifying the motions of different players in a tennis match so that an avatar can assume control of the game on the player's behalf. Steps one through four of the proposed procedure are preprocessing the data, segmenting the data, selecting features, and training the model. Preprocessing videos is used to observe people's actions. Background subtraction and GMM both play a role in the segmentation process. Parameters such as speed, measurement, and movement play a significant role in feature extraction. The models are then trained with LSTM-RNN after feature extraction. The proposed method outperforms the two most used alternatives, LSTM and RNN.