2020/03/12 by Habtamu Fanta, Zhiwen Shao, Lizhuang Ma · 53 citations
Computer Science · Mathematics · #Abnormality #Anomaly Detection Techniques and Applications #Artificial intelligence #Artificial neural network #Computer science #Context (archaeology) #Deep learning #Digital Media Forensic Detection #Generalization #Machine learning #Mathematics #Pattern recognition (psychology) #Recurrent neural network #Reset (finance) #Sigmoid function #Time Series Analysis and Forecasting #cs.CV #cs.LG
paper · pdf · doi:10.1016/j.ins.2020.03.034
published in Information Sciences 524, 15-32 (Elsevier BV) · 14 pages, 11 figures, 13 tables, this paper is accepted on Journal of Information Sciences
openalex publication_date 2020/03/12 · arxiv created 2020/03/30 · arxiv updated 2020/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abnormality detection is a challenging task due to the dependence on a specific context and the unconstrained variability of practical scenarios. In recent years, it has benefited from the powerful features learnt by deep neural networks, and handcrafted features specialized for abnormality detectors. However, these approaches with large complexity still have limitations in handling long term sequential data (e.g., videos), and their learnt features do not thoroughly capture useful information. Recurrent Neural Networks (RNNs) have been shown to be capable of robustly dealing with temporal data in long term sequences. In this paper, we propose a novel version of Gated Recurrent Unit (GRU), called Single Tunnelled GRU for abnormality detection. Particularly, the Single Tunnelled GRU discards the heavy weighted reset gate from GRU cells that overlooks the importance of past content by only favouring current input to obtain an optimized single gated cell model. Moreover, we substitute the hyperbolic tangent activation in standard GRUs with sigmoid activation, as the former suffers from performance loss in deeper networks. Empirical results show that our proposed optimized GRU model outperforms standard GRU and Long Short Term Memory (LSTM) networks on most metrics for detection and generalization tasks on CUHK Avenue and UCSD datasets. The model is also computationally efficient with reduced training and testing time over standard RNNs.