Abnormal event detection in videos using generative adversarial nets

Ravanbakhsh, Mahdyar; Nabi, Moin; Sangineto, Enver; Marcenaro, Lucio; Regazzoni, Carlo S.; Sebe, Nicu

doi:10.1109/icip.2017.8296547

Cited by 382 publications

(268 citation statements)

References 25 publications

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“…Generative adversarial network based method. Deep learning through generative adversarial networks (GANs) for abnormal event detection was introduced by Ravanbakhsh et al [29]. GANs are used to learn and model normal crowd behaviour using unsupervised data.…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

“…However, it is still a challenging problem due to two key factors. (i) The lack of large-scale annotated training data limits the performance of Deep Convolutional Neural Networks (DCNNs) [28,29]. (ii) The contentious definition of the term "abnormal" or "anomaly" causes significantly different solutions in different context (environment) [12,28,29].…”

Section: Introductionmentioning

confidence: 99%

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SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection

Fanta

Shao

2020

Information Sciences

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A B S T R A C TAbnormality 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.

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Section: Deep Learning Based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection

Fanta

Shao

2020

Information Sciences

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“…Ped2 Conv-AE [11] 0.702 0.900 Discriminative learning [7] 0.783 -Hashing filters [54] -0.910 Unmask late fusion [16] 0.806 0.822 AMDN (double fusion) [51] -0.908 ConvLSTM-AE [27] 0.770 0.881 DeepAppearance [42] 0.846 -FRCN action [14] -0.922 TSC [28] 0.806 0.910 Stacked RNN [28] 0.817 0.922 AbnormalGAN [37] -0.935 GrowingGas [46] -0.941 Future frame prediction [25] 0.851 0.954 Our proposed method 0.869 0.962 Table 1. Frame-level performance (AUC) of anomaly detection on the CUHK Avenue and UCSD Ped2 datasets.…”

Section: Avenuementioning

confidence: 99%

Anomaly Detection in Video Sequence With Appearance-Motion Correspondence

Nguyen

Meunier²

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

272

139

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Anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. We propose a deep convolutional neural network (CNN) that addresses this problem by learning a correspondence between common object appearances (e.g. pedestrian, background, tree, etc.) and their associated motions. Our model is designed as a combination of a reconstruction network and an image translation model that share the same encoder. The former sub-network determines the most significant structures that appear in video frames and the latter one attempts to associate motion templates to such structures. The training stage is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. The experiments on 6 benchmark datasets demonstrate the competitive performance of the proposed approach with respect to state-ofthe-art methods. AbstractThis supplementary material provides these contents:• ROC curves of our frame-level scores on the CUHK Avenue and UCSD Ped2 datasets, and Precision-Recall (PR) curves on the traffic datasets.

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“…In another approach, Ravanbakhsh et al [17] proposed to use the same training setup but instead of using the discriminators for detection they adapted the output of the generators. They use the difference between the generated sample and the ground truth for each channel to determine a measure for the abnormality.…”

Section: Anomaly Detection Using Gansmentioning

confidence: 99%

“…According to the number of citations, this data set is the most used benchmark for anomaly detection in surveillance. However, as some recent work reports results for Ped1 on a subset of 16 test videos [17] whereas others report results for 36 videos [12], we decided to conduct our experiments on the Ped2 subset. The metric used for quantitative comparison is the area under the receiving operating characteristic (AUC).…”

Section: Datasetmentioning

confidence: 99%

What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks

Golda¹,

Murzyn²,

Qu³

et al. 2019

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Anomaly detection plays in many fields of research, along with the strongly related task of outlier detection, a very important role. Especially within the context of the automated analysis of video material recorded by surveillance cameras, abnormal situations can be of very different nature. For this purpose this work investigates Generative-Adversarial-Network-based methods (GAN) for anomaly detection related to surveillance applications. The focus is on the usage of static camera setups, since this kind of camera is one of the most often used and belongs to the lower price segment. In order to address this task, multiple subtasks are evaluated, including the influence of existing optical flow methods for the incorporation of short-term temporal information, different forms of network setups and losses for GANs, and the use of morphological operations for further performance improvement. With these extension we achieved up to 2.4% better results. Furthermore, the final method reduced the anomaly detection error for GANbased methods by about 42.8%.

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Abnormal event detection in videos using generative adversarial nets

Cited by 382 publications

References 25 publications

SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection

SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection

Anomaly Detection in Video Sequence With Appearance-Motion Correspondence

What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks

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