Recognition of Human Activity and Abnormal Behavior using Deep Neural Network
Roberta Hlavata, Róbert Hudec, Patrik Kamencay, Peter Sýkora · 2022 ELEKTRO (ELEKTRO) · 2022
Recognizing various abnormal activities of human behavior from video is very challenging. The overall results are affected by the available data-sets. The available data-sets contain various abnormal activities, but few of them focus mainly on non-standard human behavior. In data-sets such as KTH, they focus on abnormal activities such as a sudden change in behavior or an object occurrence where it should not occur but also various changes in human interaction. The UCF-crime data-set focuses on data that are more interesting to us, such as fight, abuse, explosions or robbery etc. However, the data-set is very demanding due to the videos length, which contains a given event in just a few seconds. This may affect the overall results of the algorithm used to detect the incident. In this paper, we create a data-set dealing with abnormal activities such as robbery, fight, hijack, harassment and a normal videos. We use the created data-set when training and testing the neural network ConvLSTM (Convolutional Long Short-Term Memory). We have obtained a classification accuracy of 97.64 % on the created data-set and used architecture of neural network.