Weakly Supervised Violence Detection in Surveillance Video

Roman, David Gabriel Choqueluque; Cámara-Chávez, Guillermo

doi:10.3390/s22124502

Cited by 18 publications

(5 citation statements)

References 84 publications

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“…It should be noted that DenseNet uses multi-layer feature concatenation for improved feature representation, but this approach requires more GPU memory and longer training times. Choqueluque-Roman et al [105] followed an approach that used an I3D architecture in combination with a ResNet50 for feature extraction using human action tubes for training a deep learning model based on MIL. Their results showed that, according to the accuracy and AUC metrics, our models achieved better performance with relatively fewer model parameters, which confirms that training based on MIL may not achieve high classification accuracy.…”

Section: Methodsmentioning

confidence: 99%

Toward Fast and Accurate Violence Detection for Automated Video Surveillance Applications

et al. 2023

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Surveillance cameras are increasingly being used worldwide due to the proliferation of digital video capturing, storage, and processing technologies. However, the large volume of video data generated makes it difficult for humans to perform real-time analysis, and even manual approaches can result in delayed detection of events. Automatic violence detection in surveillance footage has therefore gained significant attention in the scientific community as a way to address this challenge. With the advancement of machine learning algorithms, automatic video recognition tasks such as violence detection have become increasingly feasible. In this study, we investigate the use of smart networks that model the dynamic relationships between actors and/or objects using 3D convolutions to capture both the spatial and temporal structure of the data. We also leverage the knowledge learned by a pre-trained action recognition model for efficient and accurate violence detection in surveillance footage. We extend and evaluate several public datasets featuring diverse and challenging video content to assess the effectiveness of our proposed methods. Our results show that our approach outperforms state-of-the-art methods, achieving approximately a 2% improvement in accuracy with fewer model parameters. Additionally, our experiments demonstrate the robustness of our approach under common compression artifacts encountered in remote server processing applications.

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Section: Methodsmentioning

confidence: 99%

Toward Fast and Accurate Violence Detection for Automated Video Surveillance Applications

et al. 2023

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“…Similarly, a conventional method was proposed in [13], utilizing motion cues derived from optical flow using RGB frames and incorporating appearance as low-level features. The system coded these qualities into a bag of words (BoWs) to eliminate redundant information, which eventually discovered violent behavior.…”

Section: A Machine Learning-based Vd Approachesmentioning

confidence: 99%

VD-Net: An Edge Vision-Based Surveillance System for Violence Detection

Khan,

Saddik,

Gueaieb

et al. 2024

IEEE Access

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The automation of surveillance systems, driven by the rapid development of computer vision technology, has significantly enhanced the analysis of surveillance videos, particularly in recognition of human activity, including behavior analysis and violence detection, thereby bolstering public and industrial security. Despite these advancements, detecting and analyzing violent actions remains challenging, especially for real-time surveillance systems with limited computing power. We propose an artificial intelligencebased framework called VD-Net (Violence Detection Network), enabled by Intelligent Internet-of-Things (IIoT) to detect violent behavior in public and private spaces. The model utilizes lightweight special task temporal convolutional network (ST-TCN) blocks and several bottleneck layers to focus on salient features in the input sequence. The learned features passed from the classifier to discriminate between violent and nonviolent actions. Additionally, our system is supposed to trigger an alert if violence is detected, which is then communicated to relevant departments. We tested the effectiveness of the proposed system by conducting experiments on surveillance and non-surveillance datasets and ensured a 1-4 % improvement in State-of-The-Art (SoTA) accuracy.

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“…The system is used for the detection of crimes. In [18], the authors have proposed a weakly supervised method to detect spatial and temporal actions that are violent in the videos. They have used the fast -RCNN architecture that extracts the spatiotemporal information.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid CRNN Model for Multi-Class Violence Detection in Text and Video

Ghadekar,

Agrawal,

Bhosale

et al. 2023

ITM Web Conf.

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Gender-based violence is a critical issue that not only poses a threat to physical safety but also has significant impacts on mental health. Shockingly, up to 1 billion children aged 2-17 years are estimated to have experienced gender-based violence globally, making it a pressing concern for the machine learning and deep learning communities to address. To end this, a novel approach has been proposed in the form of a Convolutional Neural Network and bi-directional LSTM (CRNN) to classify three types of violence present in both text and video data, thereby making the internet a safer space for individuals. The proposed approach utilises two datasets consisting of 400 and 600 samples each for videos and text, respectively, to improve the precision and accuracy of the model. The use of a Convolutional Recurrent Neural Network framework combined with LSTM layers has resulted in an accuracy of 97% on text and 96% on videos, surpassing the performance of existing RNN models. Additionally, the inclusion of dropout and regularizer layers has helped the model avoid overfitting and generalise better on unseen data. Overall, the CRNN-based approach presents a promising solution to the problem of gender-based violence detection, with the potential to significantly improve the safety of individuals online. By leveraging the power of machine learning and deep learning, we can contribute towards creating a safer and more equitable world for all.

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Weakly Supervised Violence Detection in Surveillance Video

Cited by 18 publications

References 84 publications

Toward Fast and Accurate Violence Detection for Automated Video Surveillance Applications

Toward Fast and Accurate Violence Detection for Automated Video Surveillance Applications

VD-Net: An Edge Vision-Based Surveillance System for Violence Detection

A Hybrid CRNN Model for Multi-Class Violence Detection in Text and Video

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