| Title | Anomaly Detection in Surveillance Videos |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | M.R., Anala, Makker, Malika, Ashok, Aakanksha |
| Conference Name | 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) |
| Keywords | anomalous behavior, anomaly classification, anomaly detection, classification, CNN-LSTM model, convolutional neural nets, convolutional neural networks, explosion, Explosions, feature extraction, Frames, Human Behavior, image classification, learning (artificial intelligence), learning patterns, long short-term memory networks, LSTM networks, Metrics, pubcrawl, public safety, recurrent neural nets, Resiliency, road accidents, security of data, spatial feature extraction, spatial feature learning, surveillance, surveillance videos, temporal feature learning, Training, UCF Crime dataset, video signal processing, video surveillance, Videos |
| Abstract | Every public or private area today is preferred to be under surveillance to ensure high levels of security. Since the surveillance happens round the clock, data gathered as a result is huge and requires a lot of manual work to go through every second of the recorded videos. This paper presents a system which can detect anomalous behaviors and alarm the user on the type of anomalous behavior. Since there are a myriad of anomalies, the classification of anomalies had to be narrowed down. There are certain anomalies which are generally seen and have a huge impact on public safety, such as explosions, road accidents, assault, shooting, etc. To narrow down the variations, this system can detect explosion, road accidents, shooting, and fighting and even output the frame of their occurrence. The model has been trained with videos belonging to these classes. The dataset used is UCF Crime dataset. Learning patterns from videos requires the learning of both spatial and temporal features. Convolutional Neural Networks (CNN) extract spatial features and Long Short-Term Memory (LSTM) networks learn the sequences. The classification, using an CNN-LSTM model achieves an accuracy of 85%. |
| DOI | 10.1109/HiPCW.2019.00031 |
| Citation Key | mr_anomaly_2019 |
Anomaly Detection in Surveillance Videos