Detecting non-transient anomalies in visual information using neural networks

We address the problem of detecting non-transient anomalies in visual information. By non-transient anomalies we mean changes in the way environments look that are persistent across time. Such changes may include leaving unattended bags at airport corridors, putting graffiti in building walls or damaging public property. Detecting non-transient anomalies is critical to security and surveillance in indoor and outdoor environments. We argue that existing off-the-shelf solutions to computer vision problems (e.g., image recognition, gesture recognition, text recognition) are not the most efficient when applied to detecting non-transient anomalies due to their associated computational overhead. In this paper we present a neural network-based architecture that addresses some of the limitations of the state of the art. To speed up computations, our architecture supports the processing of a large number of neurons in parallel. To reduce computational overheads, our architecture omits some of the Gaussian kernel-based feature extraction tasks performed by other systems. To classify visual anomalies as non-transient, our architecture uses a codebook-based algorithm which builds a history profile for every image segment. We describe our architecture and present some performance analysis.