Abstract
Anomaly detection in video surveillance data is very challenging due to large environmental changes and human movement. Additionally, high dimensionality of video data and video feature representation adds to these challenges. Many machine learning algorithms failed to show accurate results and it is time consuming in many cases. The semi supervised nature of deep learning algorithms aids in learning representations from the video data instead of hand crafting the features for specific scenes. Deep learning is applied to handle complicated anomalies to improve the accuracy of anomaly detection due to its efficiency in feature learning. In this paper, we propose an efficient model to predict anomaly in video surveillance data and the model is optimized by tuning the hyperparameters.
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Kavikuil, K., Amudha, J. (2019). Leveraging Deep Learning for Anomaly Detection in Video Surveillance. In: Bapi, R., Rao, K., Prasad, M. (eds) First International Conference on Artificial Intelligence and Cognitive Computing . Advances in Intelligent Systems and Computing, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-13-1580-0_23
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DOI: https://doi.org/10.1007/978-981-13-1580-0_23
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