Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder

Chong, Yong Shean; Tay, Yong Haur

doi:10.1007/978-3-319-59081-3_23

Cited by 449 publications

(297 citation statements)

References 9 publications

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“…Spatio-temporal based method. A spatio-temporal neural network architecture for anomaly detection consisting of two major components was presented by Chong et al [8], which was evaluated in multiple scenes including crowded scenes. One of the components is responsible for representing spatial features, whereas the other component learns temporal evolution of spatial features.…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

“…Regularity score is a widely used metrics to compute the consistency of a test data pattern against a pattern learnt by a trained model [8,16]. After training, we evaluate model performance by analyzing how well it is capable of detecting abnormal events (unseen during training) by providing with test data containing anomalous frames.…”

Section: Regularity Scorementioning

confidence: 99%

“…Recently, Recurrent Neural Networks (RNNs) have gaining popularity due to their ability of handling sequential data. Generalized RNN and its variant Long Short-Term Memory (LSTM) are widely used for AED and other time sequence tasks such as speech recognition [8,30]. In addition, over the past years standard RNNs and LSTMs have gone through several structural modifications which led to the rise of a simplified version of LSTM, called GRU.…”

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: Regularity Scorementioning

confidence: 99%

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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“…The success of the deep learning methods in the image processing tasks [15] and action recognition [15], [16], [18] task, motivated the researchers to apply these methods in the case of the abnormal activity recognition. Consequently, a deep auto-encoder based approach [5]- [7] has been proposed to learn the features of the normal activities automatically, but generalization of these methods for real-world scenarios is difficult.…”

Section: Related Workmentioning

confidence: 99%

3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos

Dubey

Boragule

Jeon

2019

2019 International Conference on Control, Automation and Information Sciences (ICCAIS)

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Abnormal activity detection is one of the most challenging tasks in the field of computer vision. This study is motivated by the recent state-of-art work of abnormal activity detection, which utilizes both abnormal and normal videos in learning abnormalities with the help of multiple instance learning by providing the data with video-level information. In the absence of temporal-annotations, such a model is prone to give a false alarm while detecting the abnormalities. For this reason, in this paper, we focus on the task of minimizing the false alarm rate while performing an abnormal activity detection task. The mitigation of these false alarms and recent advancement of 3D deep neural network in video action recognition task collectively give us motivation to exploit the 3D ResNet in our proposed method, which helps to extract spatial-temporal features from the videos. Afterwards, using these features and deep multiple instance learning along with the proposed ranking loss, our model learns to predict the abnormality score at the video segment level. Therefore, our proposed method 3D deep Multiple Instance Learning with ResNet (MILR) along with the new proposed ranking loss function achieves the best performance on the UCF-Crime benchmark dataset, as compared to other state-of-art methods. The effectiveness of our proposed method is demonstrated on the UCF-Crime dataset.

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“…A similar approach was applied by Chong in his paper [20]. He proposed a spatiotemporal architecture for anomaly detection which includes two main components, one for spatial feature representation, another one for learning the temporal evolution of the spatial features.…”

Section: Deep Learningmentioning

confidence: 99%

Abnormality Detection with Deep Learning

Ramésh¹

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Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder

Cited by 449 publications

References 9 publications

SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection

SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection

3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos

Abnormality Detection with Deep Learning

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