The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Improved Object Detection in Video Surveillance Using Deep Convolutional Neural Network Learning
Abstract
In this paper, a convolutional neural network is used to improve the functionality of object detection using probabilistic neural network in the analysis of surveillance images. Next, this research work attempts to examine the fundamental principles and some of the underlying theories of perceptual and neural networks are often missed. This helps you understand why, in many areas of usage, deep learning has increased. The processing of surveillance photos is obviously one of the areas most influenced by this exponential progress, particularly in the identification and detection of images. The simulation result shows that the CNN-PNN obtained improved achievements in simulation for the optimal detection of objects in video streaming or video surveillance systems. The results show that the proposed method achieves higher rate of accuracy, f-measure and reduced percentage error than other methods.
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In the citing paper
“Additionally, the use of PNNs can help to reduce the risk of overfitting to the training data until 17%, which improves the generalization performance of the model until 89%.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [81]
Citations
Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965
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Cited by
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134
“The object detection method in [81] uses a CNN with a probabilistic neural network (PNN).”
Method
Metadata
- Authors
- B, D., K, R. K., S, R., & R, R.
- Venue
- 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
- Publisher
- IEEE
- Published
- 2021-11-11
- Pages
- 1-8
- OpenAlex
- W4200129393
- Cited by
- 26
- References
- 3
- Citations by year
- 2025: 4 · 2024: 7 · 2023: 12 · 2022: 3
- FWCI
- 1.80
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- object detection, video surveillance, convolutional neural networks, probabilistic neural network, video streaming
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- The paper
- B, D., K, R. K., S, R., & R, R. (2021). Improved Object Detection in Video Surveillance Using Deep Convolutional Neural Network Learning. 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). https://doi.org/10.1109/i-smac52330.2021.9640894
