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Anomaly Detection using a Convolutional Winner-Take-All Autoencoder

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Figure

Figure 1: Overview of the method using a spatial sparsity Convolutional Winner-Take-All autoencoder for anomaly detection.
Figure 3: The architecture for a Conv-WTA autoencoder with spatial sparsity for learning motion representations.
Figure 4: Learnt deconvolutional filters of Conv-WTA trained on the UCSD Ped1 and Ped2 optical flow foreground patches: (a) visualisation of 128 filters, where flow-vector angle and magnitude is represented by hue and saturation [3] of 128 filters and (b)
Figure 5: Frame-level and pixel-level evaluation on the UCSD Ped1. The legend for the pixel-level (right) is the same as for the frame-level (left).
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