Abnormal event detection via covariance matrix for optical flow based feature

Wang, Tian; Qiao, Meina; Zhu, Aichun; Niu, Yida; Li, Ce; Snoussi, Hichem

doi:10.1007/s11042-017-5309-2

Cited by 46 publications

(22 citation statements)

References 49 publications

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“…The work in [32] proposes distribution of magnitude and orientation of local interest frame descriptor is used to learn a support vector machine based a binary classifier to detect violence events. Moreover, a feature descriptor is proposed by adopting the covariance matrix coding optical flow in multi-regions of interest to represent motion information, and then one-class support vector machine is applied to detect the abnormal events in [33]. The limitation of these approaches is that a single classifier is difficult to ensure the accuracy of classification.…”

Section: A Hand-crafted Features-based Modelsmentioning

confidence: 99%

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: A Hand-crafted Features-based Modelsmentioning

confidence: 99%

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

Deng

et al. 2020

IEEE Access

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“…In the motion based models, the trajectory based method was used to detect motions [7,8], since such representations can preserve the temporal structure of the abnormal events. The computational cost rose significantly due to occlusion in complex scenes.…”

Section: Related Workmentioning

confidence: 99%

Generative Neural Networks for Anomaly Detection in Crowded Scenes

Wang

Qiao

Lin

et al. 2019

IEEE Trans.Inform.Forensic Secur.

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132

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Security surveillance is critical to social harmony and people's peaceful life. It has a great impact on strengthening social stability and life safeguarding. Detecting anomaly timely, effectively and efficiently in video surveillance remains challenging. This paper proposes a new approach, called S 2-VAE, for anomaly detection from video data. The S 2-VAE consists of two proposed neural networks: a Stacked Fully Connected Variational AutoEncoder (SF-VAE) and a Skip Convolutional VAE (SC-VAE). The SF-VAE is a shallow generative network to obtain a Gaussian mixture like model to fit the distribution of the actual data. The SC-VAE, as a key component of S 2-VAE, is a deep generative network to take advantages of CNN, VAE and skip connections. Both SF-VAE and SC-VAE are efficient and effective generative networks and they can achieve better performance for detecting both local abnormal events and global abnormal events. The proposed S 2-VAE is evaluated using four public datasets. The experimental results show that the S 2-VAE outperforms the state-of-the-art algorithms. The code will be available publicly at https://github.com/tianwangbuaa/.

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“…Some promising researches have been proposed. In [24], researchers propose a feature descriptor, covariance matrix, which encodes optical flow and partial derivatives of adjacent frames. In [25][26][27][28], authors model motion patterns with histograms of pixel changes.…”

Section: Related Workmentioning

confidence: 99%

AED-Net: An Abnormal Event Detection Network

et al. 2019

Self Cite

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It is challenging to detect the anomaly in crowded scenes for quite a long time. In this paper, a selfsupervised framework, abnormal event detection network (AED-Net), which is composed of PCAnet and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, PCAnet is trained to extract high-level semantics of crowd's situation. Next, kPCA, a oneclass classifier, is trained to determine anomaly of the scene. In contrast to some prevailing deep learning methods, the framework is completely self-supervised because it utilizes only video sequences in a normal situation. Experiments of global and local abnormal event detection are carried out on UMN and UCSD datasets, and competitive results with higher EER and AUC compared to other state-of-the-art methods are observed. Furthermore, by adding local response normalization (LRN) layer, we propose an improvement to original AED-Net. And it is proved to perform better by promoting the framework's generalization capacity according to the experiments.

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Abnormal event detection via covariance matrix for optical flow based feature

Cited by 46 publications

References 49 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

Generative Neural Networks for Anomaly Detection in Crowded Scenes

AED-Net: An Abnormal Event Detection Network

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