| Title | An Intelligent Traffic Surveillance for Detecting Real-Time Objects Using Deep Belief Networks over Convolutional Neural Networks with improved Accuracy |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Vinod, G., Padmapriya, Dr. G. |
| Conference Name | 2022 International Conference on Business Analytics for Technology and Security (ICBATS) |
| Date Published | feb |
| Keywords | belief networks, CNN, convolutional neural networks, machine learning, Metrics, Neural networks, Novel DBN, object detection, pubcrawl, Real Time Object, Real-time Systems, surveillance, Traffic Control, traffic surveillance, Training, vision |
| Abstract | Aim: Object Detection is one of the latest topics in today's world for detection of real time objects using Deep Belief Networks. Methods & Materials: Real-Time Object Detection is performed using Deep Belief Networks (N=24) over Convolutional Neural Networks (N=24) with the split size of training and testing dataset 70% and 30% respectively. Results: Deep Belief Networks has significantly better accuracy (81.2%) compared to Convolutional Neural Networks (47.7%) and attained significance value of p = 0.083. Conclusion: Deep Belief Networks achieved significantly better object detection than Convolutional Neural Networks for identifying real-time objects in traffic surveillance. |
| DOI | 10.1109/ICBATS54253.2022.9758928 |
| Citation Key | vinod_intelligent_2022 |
An Intelligent Traffic Surveillance for Detecting Real-Time Objects Using Deep Belief Networks over Convolutional Neural Networks with improved Accuracy