Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification on Surveillance Videos

Authors

  • Venkatesan Saikrishnan Department of Computer and Information Science, Faculty of Science, Annamalai University, India
  • Mani Karthikeyan Department of Computer and Information Science, Faculty of Science, Annamalai University, India
Volume: 13 | Issue: 5 | Pages: 11747-11752 | October 2023 | https://doi.org/10.48084/etasr.6231

Abstract

Surveillance videos are recordings captured by video recording devices for monitoring and securing an area or property. These videos are frequently used in applications, involving law enforcement, security systems, retail analytics, and traffic monitoring. Surveillance videos can provide valuable visual information for analyzing patterns, identifying individuals or objects of interest, and detecting and investigating incidents. Object detection and classification on video surveillance involves the usage of computer vision techniques to identify and categorize objects within the video footage. Object detection algorithms are employed to locate and identify objects within each frame. These algorithms use various techniques, namely bounding box regression, Convolutional Neural Networks (CNNs), and feature extraction to detect objects of interest. This study presents the Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification (MFODL-RODC) method on surveillance videos. The main aim of the MFODL-RODC technique lies in the accurate classification and recognition of objects in surveillance videos. To accomplish this, the MFODL-RODC method follows a two-step process, consisting of object detection and object classification. The MFODL-RODC method uses the EfficientDet object detector for the object detection process. Besides, the classification of detected objects takes place using the Variational Autoencoder (VAE) model. The MFO algorithm is employed to enrich the performance of the VAE model. The simulation examination of the MFODL-RODC technique is performed on benchmark datasets. The extensive results accentuated the improved performance of the MFODL-RODC method over other existing algorithms with an output of 98.89%.

Keywords:

surveillance videos, object detection, deep learning, classification, computer vision

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References

P. Y. Ingle and Y.-G. Kim, "Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities," Sensors, vol. 22, no. 10, Jan. 2022, Art. no. 3862.

R. K. Tripathi, A. S. Jalal, and S. C. Agrawal, "Abandoned or removed object detection from visual surveillance: a review," Multimedia Tools and Applications, vol. 78, no. 6, pp. 7585–7620, Mar. 2019.

C. Fathy and S. N. Saleh, "Integrating Deep Learning-Based IoT and Fog Computing with Software-Defined Networking for Detecting Weapons in Video Surveillance Systems," Sensors, vol. 22, no. 14, Jan. 2022, Art. no. 5075.

Mohana and H. R. Aradhya, "Object Detection and Tracking using Deep Learning and Artificial Intelligence for Video Surveillance Applications," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 10, no. 12, Jun. 2019.

N. Funde, P. Paranjape, K. Ram, P. Magde, and M. Dhabu, "Object Detection and Tracking Approaches for Video Surveillance Over Camera Network," in 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India, Mar. 2019, pp. 1171–1176.

S. Khan and L. AlSuwaidan, "Agricultural monitoring system in video surveillance object detection using feature extraction and classification by deep learning techniques," Computers and Electrical Engineering, vol. 102, Sep. 2022, Art. no. 108201.

F. Pérez-Hernández, S. Tabik, A. Lamas, R. Olmos, H. Fujita, and F. Herrera, "Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance," Knowledge-Based Systems, vol. 194, Apr. 2020, Art. no. 105590.

S. Nuanmeesri, "A Hybrid Deep Learning and Optimized Machine Learning Approach for Rose Leaf Disease Classification," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7678–7683, Oct. 2021.

L. Loyani and D. Machuve, "A Deep Learning-based Mobile Application for Segmenting Tuta Absoluta’s Damage on Tomato Plants," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7730–7737, Oct. 2021.

M. V. Daithankar and S. D. Ruikar, "Analysis of the Wavelet Domain Filtering Approach for Video Super-Resolution," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7477–7482, Aug. 2021.

M. F. Alotaibi, M. Omri, S. Abdel-Khalek, E. Khalil, and R. F. Mansour, "Computational Intelligence-Based Harmony Search Algorithm for Real-Time Object Detection and Tracking in Video Surveillance Systems," Mathematics, vol. 10, no. 5, Jan. 2022, Art. no. 733.

B. Dhiyanesh, K. Rajesh Kanna, S. Rajkumar, and R. Radha, "Improved Object Detection in Video Surveillance Using Deep Convolutional Neural Network Learning," in 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, Aug. 2021.

M. Elhoseny, "Multi-object Detection and Tracking (MODT) Machine Learning Model for Real-Time Video Surveillance Systems," Circuits, Systems, and Signal Processing, vol. 39, no. 2, pp. 611–630, Feb. 2020.

M. Ahmadi, W. Ouarda, and A. M. Alimi, "Efficient and Fast Objects Detection Technique for Intelligent Video Surveillance Using Transfer Learning and Fine-Tuning," Arabian Journal for Science and Engineering, vol. 45, no. 3, pp. 1421–1433, Mar. 2020.

A. Appathurai, R. Sundarasekar, C. Raja, E. J. Alex, C. A. Palagan, and A. Nithya, "An Efficient Optimal Neural Network-Based Moving Vehicle Detection in Traffic Video Surveillance System," Circuits, Systems, and Signal Processing, vol. 39, no. 2, pp. 734–756, Feb. 2020.

S. Kalli, T. Suresh, A. Prasanth, T. Muthumanickam, and K. Mohanram, "An effective motion object detection using adaptive background modeling mechanism in video surveillance system," Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1777–1789, Jan. 2021.

F. Joy and D. V. Vijayakumar, "An improved Gaussian Mixture Model with post-processing for multiple object detection in surveillance video analytics," International Journal of Electrical and Computer Engineering Systems, vol. 13, no. 8, pp. 653–660, Oct. 2022.

Y. Ye et al., "An Adaptive Attention Fusion Mechanism Convolutional Network for Object Detection in Remote Sensing Images," Remote Sensing, vol. 14, no. 3, Jan. 2022, Art. no. 516.

O. Boyar, K. Iwata, H. Hanada, and I. Takeuchi, "Sample-Efficient De Novo Chemical Design with Latent Reconstruction-Aware Variational Autoencoder," presented at the 37th Annual Conference of the Japanese Society for Artificial Intelligence, Apr. 2023.

N. Alqahtani et al., "Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification," Applied Sciences, vol. 13, no. 13, Jan. 2023, Art. no. 7833.

"UCSD Anomaly Detection Dataset." http://www.svcl.ucsd.edu/projects/anomaly/dataset.htm.

I. V. Pustokhina, D. A. Pustokhin, T. Vaiyapuri, D. Gupta, S. Kumar, and K. Shankar, "An automated deep learning based anomaly detection in pedestrian walkways for vulnerable road users safety," Safety Science, vol. 142, Oct. 2021, Art. no. 105356.

M. Xu, X. Yu, D. Chen, C. Wu, and Y. Jiang, "An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders," Applied Sciences, vol. 9, no. 16, Jan. 2019, Art. no. 3337.

B. S. Murugan, M. Elhoseny, K. Shankar, and J. Uthayakumar, "Region-based scalable smart system for anomaly detection in pedestrian walkways," Computers & Electrical Engineering, vol. 75, pp. 146–160, May 2019.

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How to Cite

[1]
Saikrishnan, V. and Karthikeyan, M. 2023. Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification on Surveillance Videos. Engineering, Technology & Applied Science Research. 13, 5 (Oct. 2023), 11747–11752. DOI:https://doi.org/10.48084/etasr.6231.

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