CUE-Net: Efficient Violence Detection in Video Surveillance using Spatial Cropping, Enhanced UniformerV2, and Modified Efficient Additive Attention

แนวคิดหลัก

CUE-Net, a novel architecture that combines spatial cropping, an enhanced version of the UniformerV2 model, and a Modified Efficient Additive Attention mechanism, achieves state-of-the-art performance on violence detection in video surveillance datasets.

บทคัดย่อ

The paper introduces CUE-Net, a novel architecture for efficient violence detection in video surveillance. The key components of CUE-Net are:

Spatial Cropping Module: This module uses the YOLO V8 algorithm to detect people in the video frames and crops the video to focus on the areas where violence is likely to occur, without losing important surrounding information.

3D Convolution Backbone: This module encodes and projects the spatially cropped video frames into spatio-temporal tokens.

Local UniBlock V2: This module captures the local dependencies in the video using two types of Multi-Head Relation Aggregator (MHRA) units and a Feed Forward Network (FFN).

Global UniBlock V3: This module captures the global spatio-temporal dependencies using a Dynamic Positional Embedding (DPE) unit, a novel Modified Efficient Additive Attention (MEAA) mechanism, and an FFN.

Fusion Block: This block integrates the outputs of the Local UniBlock V2 and Global UniBlock V3 to obtain the final video classification.

The authors demonstrate that CUE-Net outperforms state-of-the-art methods on the RWF-2000 and RLVS datasets, achieving accuracies of 94.00% and 99.50%, respectively. The ablation studies show the importance of the spatial cropping module and the MEAA mechanism in improving the performance and efficiency of the model.

สถิติ

Surveillance cameras are becoming more prevalent, leading to the need for automated violence detection in video.
The RWF-2000 dataset contains 2,000 trimmed video clips of real-world fighting scenarios, with a 80%-20% train-test split.
The RLVS dataset contains 2,000 video clips of real-life violence situations, also with a 80%-20% train-test split.

คำพูด

"CUE-Net addresses this challenge by combining spatial Cropping with an enhanced version of the UniformerV2 architecture, integrating convolutional and self-attention mechanisms alongside a novel Modified Efficient Additive Attention mechanism (which reduces the quadratic time complexity of self-attention) to effectively and efficiently identify violent activities."
"This approach aims to overcome traditional challenges such as capturing distant or partially obscured subjects within video frames."

ข้อมูลเชิงลึกที่สำคัญจาก

CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention

by Damith Chama... ที่ arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.18952.pdf

สอบถามเพิ่มเติม

What other types of video data, beyond surveillance footage, could CUE-Net be applied to for violence detection

CUE-Net, with its focus on combining spatial cropping with convolutional and attention mechanisms, can be applied to various types of video data beyond surveillance footage for violence detection. One potential application could be in the analysis of social media content. With the prevalence of user-generated videos on platforms like Facebook, Instagram, and TikTok, there is a growing need to monitor and detect violent content in these videos. CUE-Net could be utilized to automatically identify and flag videos containing violent behavior, helping to enforce community guidelines and ensure a safer online environment. Additionally, CUE-Net could be applied in forensic video analysis to assist law enforcement agencies in identifying violent acts in recorded footage from various sources, such as smartphones, dashcams, or body cameras.

How could the spatial cropping mechanism be further improved to better focus on the relevant areas of the video frames

To further improve the spatial cropping mechanism in CUE-Net for better focusing on the relevant areas of video frames, several enhancements could be considered:

Dynamic Cropping: Implementing a dynamic cropping strategy that adjusts the size and position of the cropped area based on the movement and location of the subjects in the video. This adaptive approach can ensure that the violent activities are always centered in the cropped region.
Object Detection Integration: Integrating advanced object detection algorithms to not only identify people but also specific objects or actions related to violence. This can help in refining the cropping process to include relevant contextual information.
Semantic Segmentation: Utilizing semantic segmentation techniques to differentiate between different elements in the video frames, allowing for more precise cropping around the areas of interest while excluding irrelevant background elements.
Attention Mechanisms: Incorporating attention mechanisms within the cropping module to dynamically adjust the focus based on the importance of different regions within the frame. This can help in capturing critical details related to violent incidents.

How might CUE-Net's performance and efficiency be impacted by incorporating additional modalities, such as audio or text, to provide more contextual information about the violent incidents

Incorporating additional modalities such as audio or text alongside video data in CUE-Net can provide valuable contextual information about violent incidents, potentially enhancing its performance and efficiency:

Audio Analysis: By analyzing audio cues such as screams, shouts, or sounds of physical altercations, CUE-Net can gain a deeper understanding of the intensity and nature of violent events. This audio-visual fusion can improve the accuracy of violence detection by capturing both visual and auditory cues.
Textual Context: Integrating text analysis from video captions, comments, or metadata can offer insights into the context surrounding the video content. Understanding the textual descriptions associated with videos can help CUE-Net differentiate between staged performances and real violent incidents, leading to more precise detection outcomes.
Multimodal Fusion: Implementing a multimodal fusion approach that combines information from video, audio, and text modalities can provide a comprehensive view of the content, enabling CUE-Net to make more informed decisions about violence detection. Techniques like late fusion or early fusion can be employed to effectively combine information from different modalities while maintaining efficiency.