Anomalous Human Action Detection Using a Cascade of Deep Learning Models

Riaz, Hamza; Uzair, Muhammad; Ullah, Habib; Ullah, Mohib

doi:10.1109/euvip50544.2021.9484062

Cited by 13 publications

(9 citation statements)

References 21 publications

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“…Ullah et al [37] introduced a Convolution-Block-Attention-based LSTM model that enhances spatial information accuracy. Riaz et al [38] combined human posture estimation with a densely connected fully Convolutional Neural Network (CNN) for anomaly identification. Hasan et al [1] utilized a recurrent neural net-work (RNN) and a convolutional autoencoder for anomaly detection, while Liu et al [39] integrated temporal and spatial detectors for anomaly identification.…”

Section: Related Workmentioning

confidence: 99%

Edge-Enhanced TempoFuseNet: A Two-Stream Framework for Intelligent Multiclass Video Anomaly Recognition in 5G and IoT Environments

Saleem,

Bajwa,

Raza

et al. 2024

Future Internet

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Surveillance video analytics encounters unprecedented challenges in 5G and IoT environments, including complex intra-class variations, short-term and long-term temporal dynamics, and variable video quality. This study introduces Edge-Enhanced TempoFuseNet, a cutting-edge framework that strategically reduces spatial resolution to allow the processing of low-resolution images. A dual upscaling methodology based on bicubic interpolation and an encoder–bank–decoder configuration is used for anomaly classification. The two-stream architecture combines the power of a pre-trained Convolutional Neural Network (CNN) for spatial feature extraction from RGB imagery in the spatial stream, while the temporal stream focuses on learning short-term temporal characteristics, reducing the computational burden of optical flow. To analyze long-term temporal patterns, the extracted features from both streams are combined and routed through a Gated Recurrent Unit (GRU) layer. The proposed framework (TempoFuseNet) outperforms the encoder–bank–decoder model in terms of performance metrics, achieving a multiclass macro average accuracy of 92.28%, an F1-score of 69.29%, and a false positive rate of 4.41%. This study presents a significant advancement in the field of video anomaly recognition and provides a comprehensive solution to the complex challenges posed by real-world surveillance scenarios in the context of 5G and IoT.

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Section: Related Workmentioning

confidence: 99%

Edge-Enhanced TempoFuseNet: A Two-Stream Framework for Intelligent Multiclass Video Anomaly Recognition in 5G and IoT Environments

Saleem,

Bajwa,

Raza

et al. 2024

Future Internet

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“…For instance, they develop a method based on ensemble for identifying regular and aberrant expression of gene patterns by employing traditional cluster algorithms as well as principal component analysis (PCA). Riaz et al [21] suggested deep ensemble-based methods for anomaly detection in complicated scenarios. Furthermore, to identify human being joints, a position-based estimation technique is integrated.…”

Section: Conventional Feature-based Approachesmentioning

confidence: 99%

An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

Amin¹,

Kim²,

Sami³

et al. 2023

Computer Systems Science and Engineering

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In the present technological world, surveillance cameras generate an immense amount of video data from various sources, making its scrutiny tough for computer vision specialists. It is difficult to search for anomalous events manually in these massive video records since they happen infrequently and with a low probability in real-world monitoring systems. Therefore, intelligent surveillance is a requirement of the modern day, as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies. In this article, we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video (ADSV). At the input of the ADSV, a shots boundary detection technique is used to segment prominent frames. Next, The Lightweight Convolution Neural Network (LWCNN) model receives the segmented frames to extract spatial and temporal information from the intermediate layer. Following that, spatial and temporal features are learned using Long Short-Term Memory (LSTM) cells and Attention Network from a series of frames for each anomalous activity in a sample. To detect motion and action, the LWCNN received chronologically sorted frames. Finally, the anomaly activity in the video is identified using the proposed trained ADSV model. Extensive experiments are conducted on complex and challenging benchmark datasets. In addition, the experimental results have been compared to state-ofthe-art methodologies, and a significant improvement is attained, demonstrating the efficiency of our ADSV method.

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“…The model proposed can identify anomalies in an occluded environment at a low computational cost. H. Riaz et al [16] detect unusual behavior of candidates in examination halls by cascading deep CNN models. The methodology uses the OpenPose algorithm to extract key body points, which are further used to retrieve patches.…”

Section: Related Workmentioning

confidence: 99%

“…Fatemah and Mahdi [8], A.B. Sargano et al [10], Donahue et al [7], Kanagaraj and Priya [11], Guillermo A. et al [12], H Riaz et al [16], and Ruchi and Manish [17] have taken advantage of various deep neural architectures to encode action dynamics in accordance with human behavior.…”

Section: Introductionmentioning

confidence: 99%

Detecting abnormal behavior in megastore for intelligent surveillance through 3D deep convolutional model

Ansari

Singh

2023

Journal of Electrical Engineering

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The use of neural networks in a range of academic and scientific pursuits has introduced a great interest in modeling human behavior and activity patterns to recognize particular events. Various methods have so far been proposed for building expert vision systems to understand the scene and draw true semantic inferences from the observed dynamics. However, classifying abnormal or unusual activities in real-time video sequences is still challenging, as the details in video sequences have a time continuity constraint. A cost-effective approach is still demanding and so this work presents an advanced three-dimensional convolutional network (A3DConvNet) for detecting abnormal behavior of persons by analyzing their actions. The network proposed is 15 layers deep that uses 18 convolutional operations to effectively analyze the video contents and produces spatiotemporal features. The integrated dense layer uses these features for the efficient learning process and the softmax layer is used as the output layer for labeling the sequences. Additionally, we have created a dataset that carries video clips to represent abnormal behaviors of humans in megastores/shops, which is a consequent contribution of this paper. The dataset includes five complicated activities in the shops/megastores: normal, shoplifting, drinking, eating, and damaging. By analyzing human actions, the proposed algorithm produces an alert if anything like abnormalities is found. The extensive experiments performed on the synthesized dataset demonstrate the effectiveness of our method, with achieved accuracy of up to 90.90%.

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Anomalous Human Action Detection Using a Cascade of Deep Learning Models

Cited by 13 publications

References 21 publications

Edge-Enhanced TempoFuseNet: A Two-Stream Framework for Intelligent Multiclass Video Anomaly Recognition in 5G and IoT Environments

Edge-Enhanced TempoFuseNet: A Two-Stream Framework for Intelligent Multiclass Video Anomaly Recognition in 5G and IoT Environments

An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

Detecting abnormal behavior in megastore for intelligent surveillance through 3D deep convolutional model

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