ANOMALYNET: AN ANOMALY DETECTION NETWORK FOR VIDEO SURVEILLANCE

Abstract

Sparse coding based anomaly detection has shown promising performance, of which the keys are
feature learning, sparse representation, and dictionary learning. In this work, we propose a new neural
network for anomaly detection (termed AnomalyNet) by deeply achieving feature learning, sparse
representation and dictionary learning in three joint neural processing blocks. Specifically, to learn
better features, we design a motion fusion block accompanied by a feature transfer block to enjoy the
advantages of eliminating noisy background, capturing motion and alleviating data deficiency.
Furthermore, to address some disadvantages (e.g., nonadaptive updating) of existing sparse coding
optimizers and embrace the merits of neural network (e.g., parallel computing), we design a novel
recurrent neural network to learn sparse representation and dictionary by proposing an adaptive
iterative hard- thresholding algorithm (adaptive ISTA) and reformulating the adaptive ISTA as a new
long short term memory (LSTM)