Biblio
Filters: Keyword is Real Time Object [Clear All Filters]
.
2022. An Intelligent Traffic Surveillance for Detecting Real-Time Objects Using Deep Belief Networks over Convolutional Neural Networks with improved Accuracy. 2022 International Conference on Business Analytics for Technology and Security (ICBATS). :1–4.
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.