Abnormal Event Detection on BMTT-PETS 2017 Surveillance Challenge

Vignesh, Kothapalli,; Yadav, Gaurav; Sethi, Amit

doi:10.1109/cvprw.2017.268

Cited by 17 publications

(15 citation statements)

References 22 publications

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“…For example, a convolutional autoencoder integrates with a long short-term memory model to detect abnormal events in video surveillance in [46]. Kothapalli et al [47] use mixture of Gaussians to subtract the background of each frame first, and then a convolutional neural network is used to extract spatial features that are fed into long short-term memory to learn temporal features. Finally, a linear support vector machine is used to classify to detect abnormal events.…”

Section: B Deep Appearance Features-based Modelsmentioning

confidence: 99%

“…Convolutional neural networks are a kind of common deep neural network, which are suitable for spatial relationships learning on raw input data. Among the various convolutional neural network models, a convolutional neural network named VGG-16 can be employed to extract spatial features as well as for high accuracy image recognition because of the depth of network [47], and therefore it can be applied to feature extraction for complex video surveillance scenes. However, the VGG-16 network is difficult to represent the temporal relationship of the input video sequences accurately.…”

Section: A Cnn-lstm Feature Extractionmentioning

confidence: 99%

“…We conduct extensive experiments on a widely used abnormal event detection dataset and a coal mining video dataset to evaluate the performance of the proposed DF-ESCC and compare them with several state-of-the-art methods such as SURF+BoW, SIFT+BoW [16], HMM with optical flow [34], CNN-2D+LSTM [43] and CNN-2D+LSTM+SVM [47]. All the experiments are conducted on a machine having a Inter Core (TM) i7-7700HQ processor with 8G memory and a Huawei server having 4-Inter Xeon processors with 8G memory, respectively.…”

Section: Experimental Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

Deng

et al. 2020

IEEE Access

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At present, the existing abnormal event detection models based on deep learning mainly focus on data represented by a vectorial form, which pay little attention to the impact of the internal structure characteristics of feature vector. In addition, a single classifier is difficult to ensure the accuracy of classification. In order to address the above issues, we propose an abnormal event detection hybrid modulation method via feature expectation subgraph calibrating classification in video surveillance scenes in this paper. Our main contribution is to calibrate the classification of a single classifier by constructing feature expectation subgraphs. First, we employ convolutional neural network and long short-term memory models to extract the spatiotemporal features of video frame, and then construct the feature expectation subgraph for each key frame of every video, which could be used to capture the internal sequential and topological relational characteristics of structured feature vector. Second, we project expectation subgraphs on the sparse vector to combine with a support vector classifier to calibrate the results of a linear support vector classifier. Finally, the experiments on a common dataset named UCSDped1 and a coal mining video dataset in comparison with some existing works demonstrate that the performance of the proposed method is better than several the state-of-the-art approaches. INDEX TERMS Abnormal event detection, feature expectation subgraph, calibrating classification, sequential and topological relational characteristics.

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Section: B Deep Appearance Features-based Modelsmentioning

confidence: 99%

Section: A Cnn-lstm Feature Extractionmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

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Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

Deng

et al. 2020

IEEE Access

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“…Research has shown that a hybrid approach consisting of CNN and LSTM (aka, the Conv-LSTM network) shows a very powerful response and leads to high confidence in solving research problems such as video classification [11], sentiment [12], emotion recognition [13]; and in anomalous incident detection from a video [14]. Thus, to enhance the learning capability and detection performance of IDS, we propose a deep learning-based IDS system.…”

Section: Introductionmentioning

confidence: 99%

A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network

2019

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With the rapid advancements of ubiquitous information and communication technologies, a large number of trustworthy online systems and services have been deployed. However, cybersecurity threats are still mounting. An intrusion detection (ID) system can play a significant role in detecting such security threats. Thus, developing an intelligent and accurate ID system is a non-trivial research problem. Existing ID systems that are typically used in traditional network intrusion detection system often fail and cannot detect many known and new security threats, largely because those approaches are based on classical machine learning methods that provide less focus on accurate feature selection and classification. Consequently, many known signatures from the attack traffic remain unidentifiable and become latent. Furthermore, since a massive network infrastructure can produce large-scale data, these approaches often fail to handle them flexibly, hence are not scalable. To address these issues and improve the accuracy and scalability, we propose a scalable and hybrid IDS, which is based on Spark ML and the convolutional-LSTM (Conv-LSTM) network. This IDS is a two-stage ID system: the first stage employs the anomaly detection module, which is based on Spark ML. The second stage acts as a misuse detection module, which is based on the Conv-LSTM network, such that both global and local latent threat signatures can be addressed. Evaluations of several baseline models in the ISCX-UNB dataset show that our hybrid IDS can identify network misuses accurately in 97.29% of cases and outperforms state-of-the-art approaches during 10-fold cross-validation tests.

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“…Kothapalli Vignesh et al, [13] had utilised the supervised learning to develop a strategy that learns with a limited number of videos by isolating the normal and abnormal action. First, they subtracted the background of each frame by modelling each pixel as a mixture of Gaussians (MoG).…”

Section: Supervised Approachmentioning

confidence: 99%

Abnormality Detection with Deep Learning

Ramésh¹

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Abnormal Event Detection on BMTT-PETS 2017 Surveillance Challenge

Cited by 17 publications

References 22 publications

Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network

Abnormality Detection with Deep Learning

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