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Abnormality detection is a challenging task due to the dependence on aspecific context and the unconstrained variability of practical scenarios. Inrecent years, it has benefited from the powerful features learnt by deep neuralnetworks, and handcrafted features specialized for abnormality detectors.However, these approaches with large complexity still have limitations inhandling long term sequential data (e.g., videos), and their learnt features donot thoroughly capture useful information. Recurrent Neural Networks (RNNs)have been shown to be capable of robustly dealing with temporal data in longterm sequences. In this paper, we propose a novel version of Gated RecurrentUnit (GRU), called Single Tunnelled GRU for abnormality detection.Particularly, the Single Tunnelled GRU discards the heavy weighted reset gatefrom GRU cells that overlooks the importance of past content by only favouringcurrent input to obtain an optimized single gated cell model. Moreover, wesubstitute the hyperbolic tangent activation in standard GRUs with sigmoidactivation, as the former suffers from performance loss in deeper networks.Empirical results show that our proposed optimized GRU model outperformsstandard GRU and Long Short Term Memory (LSTM) networks on most metrics fordetection and generalization tasks on CUHK Avenue and UCSD datasets. The modelis also computationally efficient with reduced training and testing time overstandard RNNs.

SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection