Computer Science > Computer Vision and Pattern Recognition
arXiv:2203.03872 (cs)
[Submitted on 8 Mar 2022]
Title:Visual anomaly detection in video by variational autoencoder
Authors:Faraz Waseem (yahoo),Rafael Perez Martinez (Stanford University),Chris Wu (Stanford University)
View a PDF of the paper titled Visual anomaly detection in video by variational autoencoder, by Faraz Waseem (yahoo) and 2 other authors
View PDFAbstract:Video anomalies detection is the intersection of anomaly detection and visual intelligence. It has commercial applications in surveillance, security, self-driving cars and crop monitoring. Videos can capture a variety of anomalies. Due to efforts needed to label training data, unsupervised approaches to train anomaly detection models for videos is more practical An autoencoder is a neural network that is trained to recreate its input using latent representation of input also called a bottleneck layer. Variational autoencoder uses distribution (mean and variance) as compared to latent vector as bottleneck layer and can have better regularization effect. In this paper we have demonstrated comparison between performance of convolutional LSTM versus a variation convolutional LSTM autoencoder
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2203.03872 [cs.CV] |
| (orarXiv:2203.03872v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2203.03872 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Visual anomaly detection in video by variational autoencoder, by Faraz Waseem (yahoo) and 2 other authors
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