| Title | Usual and Unusual Human Activity Recognition in Video using Deep Learning and Artificial Intelligence for Security Applications |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Sunil, Ajeet, Sheth, Manav Hiren, E, Shreyas, Mohana |
| Conference Name | 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) |
| Keywords | activity recognition, Artificial Intelligence (AI), CCTV, Computer Vision (CV), convolutional neural network (CNN), Deep Learning, deep video, Detectors, Metrics, pubcrawl, resilience, Resiliency, Scalability, Single Shot Detector (SSD), tensors, Tools, transfer learning, video surveillance, visualization |
| Abstract | The main objective of Human Activity Recognition (HAR) is to detect various activities in video frames. Video surveillance is an import application for various security reasons, therefore it is essential to classify activities as usual and unusual. This paper implements the deep learning model that has the ability to classify and localize the activities detected using a Single Shot Detector (SSD) algorithm with a bounding box, which is explicitly trained to detect usual and unusual activities for security surveillance applications. Further this model can be deployed in public places to improve safety and security of individuals. The SSD model is designed and trained using transfer learning approach. Performance evaluation metrics are visualised using Tensor Board tool. This paper further discusses the challenges in real-time implementation. |
| DOI | 10.1109/ICECCT52121.2021.9616791 |
| Citation Key | sunil_usual_2021 |
Usual and Unusual Human Activity Recognition in Video using Deep Learning and Artificial Intelligence for Security Applications