Open AccessA Novel Framework for Anomaly Detection in Video Surveillance using Convolutional LSTM
Author(s) -
Lovleen siddhu,
Ranganathan Sridhar
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.d6476.049420
Subject(s) - anomaly detection , computer science , artificial intelligence , encoder , video tracking , object detection , event (particle physics) , computer vision , identification (biology) , tracking (education) , object (grammar) , pattern recognition (psychology) , real time computing , physics , botany , quantum mechanics , biology , operating system , psychology , pedagogy
Today, due to public safety requirements, surveillance systems have gained increased attention. Video data processing technologies such as the identification of activity [1], object tracking [2], crowd counting [3], and the detection of anomalies [ 4] have therefore been rapidly developing. In this study, we establish an unattended method for the detection of anomaly events in videos based on a ConvLSTM encoder-decoder to learn about the evolution of spatial characteristics. Our model only covers typical video events during preparation, whereas in testing the videos are both usual and abnormal. Experiments on the UCSD datasets confirm the validity of the suggested approach to abnormal event detection.