CrowdVAS-Net: A Deep-CNN Based Framework to Detect Abnormal Crowd-Motion Behavior in Videos for Predicting Crowd Disaster

Gupta, Tanu; Roy, Sudip

doi:10.1109/smc.2019.8914152

Cited by 22 publications

(15 citation statements)

References 30 publications

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“…Moreover, the authors of [37] propose a deep neural architecture which can be used for crowd evacuation. Also, authors of [38] and [39] have used DNN to predict the number of people in an area. CNNs: CNN can classify images with better accuracy and are better at capturing orientation than ANN.…”

Section: Machine Learning Overviewmentioning

confidence: 99%

Section: Machine Learning Overviewmentioning

confidence: 99%

“…Gupta et al [39] have proposed a model which analyses video of the crowd in an area. It uses a deep CNN model to classify video features, such as the speed of the crowd and physical features of the environment to classify the crowd’s behavior as a normal situation or an alert situation.…”

Section: Applications Of Machine Learning Models In Crowd Evacuationmentioning

confidence: 99%

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Disaster and Pandemic Management Using Machine Learning: A Survey

Chamola

Hassija

Gupta

et al. 2021

IEEE Internet Things J.

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This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

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Section: Machine Learning Overviewmentioning

confidence: 99%

Section: Machine Learning Overviewmentioning

confidence: 99%

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Disaster and Pandemic Management Using Machine Learning: A Survey

Chamola

Hassija

Gupta

et al. 2021

IEEE Internet Things J.

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show abstract

“…It achieves great precision as an outcome of its advantage in merging the results of whole decision trees. Furthermore, according to [34], the random forest algorithm outperforms KNN, decision tree, and SVM with a precision of 77.8%.…”

Section: State-of-the-artmentioning

confidence: 99%

Machine Learning Based Real Time-Heuristic Sensor Data Analytics For Early Warning Prediction

Pal¹,

Mishra²,

Haldorai³

et al. 2021

Preprint

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Landslides have the potential to cause significant property damage as well as fatalities. Landslides are identified by real-time heuristic data analysis acquired using wireless sensor networks (WSNs) in changing environments. People can abandon dangerous locations earlier when landslides are forecasted. In this paper, the early warning prediction system developed using machine learning; Artificial Neural Networks (ANNs) provide precise predictions. The weight coefficients of ANN can be adjusted exactly enough by network functional training. In the case of unbalanced data distribution, the proposed ANN model is unable to learn the sample data pattern. This results in incorrect prediction and therefore, a switching method is utilized to switch between alternative predictors based on the current environmental state. Furthermore, the proposed model has been developed to forecast and compensate for errors during the prediction phase. Thus, the proposed model can enhance precise prediction, and an early warning prediction system of landslides can issue warnings 44.2 minutes before a landslide occurrence.

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“…In order to overcome the drawback of hand-crafted features, many models based on deep appearance features are proposed to detect abnormal events. The above deep appearance features can be obtained by using convolutional neural networks [36], [37], recurrent neural networks [38], [39] and autoencoder networks [40], [41].…”

Section: B Deep Appearance 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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CrowdVAS-Net: A Deep-CNN Based Framework to Detect Abnormal Crowd-Motion Behavior in Videos for Predicting Crowd Disaster

Cited by 22 publications

References 30 publications

Disaster and Pandemic Management Using Machine Learning: A Survey

Disaster and Pandemic Management Using Machine Learning: A Survey

Machine Learning Based Real Time-Heuristic Sensor Data Analytics For Early Warning Prediction

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

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