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Feature selection algorithm assisted residual channel attention spatio-temporal auto encoder for video anomaly detection

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Abstract

The common use of video cameras in public and private spaces for traffic management, security, and anomaly detection has brought significant benefits. However, automatically detecting anomalous activity within this vast amount of video data offers a significant challenge. Existing video anomaly detection algorithms have limitations such as high computational cost, low performance, and a large demand for specific hardware resources. To overcome these drawbacks, this paper introduces a novel autoencoder model for accurate anomaly detection. First, the University of Central Florida (UCF) crime dataset is used to collect input data. Input videos are transformed into frames; then, keyframes are chosen from the Cosine Similarity distance measure. Selected keyframes are fed into the pre-processing stage to improve the brightness-guided pre-processing procedure for enhancing the brightness variability of keyframes. Optimal features from the pre-processed frames were taken using the chaotic gazelle optimization algorithm (CGOA) to reduce training time. Finally, residual channel attention assisted spatial–temporal autoencoder (RCAA-STAE) in detecting anomaly classes. The performance of the proposed model is estimated and compared with previous studies. The proposed model attained effective performance results in terms of accuracy, precision, recall, specificity, kappa, Root mean square error (RMSE), area under the curve (AUC), and mean square error (MSE). Accuracy performance attained by the proposed approach is 99.23% and 99.5% for normal and abnormal classes using the UCF-crime dataset.

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Authors and Affiliations

  1. Department of CSE, BVRIT HYDERABAD College of Engineering for Women, Hyderabad, 500090, India

    M. Lakshmi Prasudha, Vidyullatha Sukhavasi, Kandula Neha & Poonam Shaylesh Lunawat

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  1. M. Lakshmi Prasudha
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  2. Vidyullatha Sukhavasi
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  3. Kandula Neha
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  4. Poonam Shaylesh Lunawat
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Contributions

"1M.Lakshmi Prasudha Review and Editing Preparation, Software, Formal analysis, Investigation, *2 Vidyullatha Sukhavasi Supervision, Project administration, Specifically Writing the Initial Draft, Resources. 3Kandula Neha Specifically Critical Review, Visualization, Methodology, Commentary. 4Poonam Shaylesh Lunawat Conceptualization, Writing–Original Draft Preparation, Data Curation, Validation.All authors reviewed the manuscript."

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Correspondence to Vidyullatha Sukhavasi.

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Prasudha, M.L., Sukhavasi, V., Neha, K. et al. Feature selection algorithm assisted residual channel attention spatio-temporal auto encoder for video anomaly detection. SIViP 19, 148 (2025). https://doi.org/10.1007/s11760-024-03640-0

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  • DOI: https://doi.org/10.1007/s11760-024-03640-0

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