Name
Abstract | ||
|---|---|---|
Different-sized anomalies and its occurrence in a shorter period have always been an open research issue. To resolve the issue of detecting anomalies of different sizes, especially in pedestrian pathways, within a shorter time period, the current research article introduced a Region based Scalable Convolution Neural Network (RS-CNN). The proposed method used region based proposals for faster identification and performed well with the scalability issues. The RS-CNN model was validated using different video sequences from the UCSD anomaly detection dataset. When compared with state-of-the-art detection techniques such as Fast R-CNN, Minimization of Drive Testing (MDT), Mixtures of Probabilistic Principal Component Analyzers (MPPCA) and Social Force (SF), the RS-CNN model was found to be faster and efficient even in the presence of anomalies of various sizes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compeleceng.2019.02.017 | Computers & Electrical Engineering |
Keywords | Field | DocType |
Artificial intelligence,Anomaly detection,Convolution neural network,Computer vision,Pedestrian walkways | Open research,Anomaly detection,Smart system,Convolutional neural network,Computer science,Real-time computing,Minification,Probabilistic logic,Principal component analysis,Scalability | Journal |
Volume | ISSN | Citations |
75 | 0045-7906 | 1 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| B. S. Murugan | 1 | 4 | 0.73 |
| mohamed elhoseny | 2 | 583 | 49.57 |
| K. Shankar | 3 | 95 | 13.88 |
| J. Uthayakumar | 4 | 3 | 1.05 |