| Title | Application of Deep Learning for Crowd Anomaly Detection from Surveillance Videos |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Pawar, Karishma, Attar, Vahida |
| Conference Name | 2021 11th International Conference on Cloud Computing, Data Science Engineering (Confluence) |
| Keywords | anomaly detection, Deep Learning, deep video, Error analysis, Metrics, one-class classification, pubcrawl, resilience, Resiliency, Scalability, security, surveillance, unsupervised learning, video surveillance, visual analytics |
| Abstract | Due to immense need for implementing security measures and control ongoing activities, intelligent video analytics is regarded as one of the outstanding and challenging research domains in Computer Vision. Assigning video operator to manually monitor the surveillance videos 24x7 to identify occurrence of interesting and anomalous events like robberies, wrong U-turns, violence, accidents is cumbersome and error- prone. Therefore, to address the issue of continuously monitoring surveillance videos and detect the anomalies from them, a deep learning approach based on pipelined sequence of convolutional autoencoder and sequence to sequence long short-term memory autoencoder has been proposed. Specifically, unsupervised learning approach encompassing one-class classification paradigm has been proposed for detection of anomalies in videos. The effectiveness of the propped model is demonstrated on benchmarked anomaly detection dataset and significant results in terms of equal error rate, area under curve and time required for detection have been achieved. |
| DOI | 10.1109/Confluence51648.2021.9377055 |
| Citation Key | pawar_application_2021 |
Application of Deep Learning for Crowd Anomaly Detection from Surveillance Videos