Surveillance Video-Based Accident Detection Using Transformer Architecture Achieves Robust Performance in Diverse Traffic Environments
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Road traffic accidents pose a significant global threat, with increasing rates demanding improvements in surveillance technology, and a team led by Tanu Singh and Pranamesh Chakraborty from the Indian Institute of Technology Kanpur, alongside Long T. Truong from La Trobe University, now presents a novel approach to automated accident detection. Traditional computer vision systems often struggle to accurately interpret complex traffic scenarios, lacking the ability to fully understand both spatial and temporal relationships within video footage, and existing datasets limit the development of robust, generalizable systems.
This research overcomes these challenges by introducing a new accident detection model built on a transformer architecture, trained on a comprehensive and diverse dataset of traffic footage, and crucially, incorporating motion cues often overlooked in previous studies. The resulting system achieves high accuracy, surpassing the performance of leading vision language models, and represents a substantial step towards more effective and reliable traffic surveillance.
Hybrid System Detects Road Accidents Automatically Researchers have developed a system for automatically detecting road accidents using surveillance video, addressing a critical need for improved traffic monitoring, faster emergency response times, and enhanced road safety.
The team proposed a novel hybrid approach that effectively combines spatial and temporal information from video footage, accurately identifying accidents regardless of traffic conditions or incident type. At the heart of the system lies a combination of Convolutional Neural Networks and Transformers.
Convolutional Neural Networks analyze individual video frames, identifying objects and scene characteristics, while Transformers model relationships between frames, capturing motion and traffic dynamics. By combining these technologies, the system creates a comprehensive understanding of the video sequence.
The team investigated the integration of motion cues, utilizing optical flow to capture movement and improve accuracy. Experiments revealed that combining standard RGB features with optical flow data yielded the best results, achieving an F1-score of 88. 3% on a dedicated test dataset. This performance surpasses that of several state-of-the-art vision-language models, including GPT-5 and LLaVA-Next-Video, and significantly outperforms existing methods.
This research introduces a novel hybrid architecture that effectively combines spatial and temporal features for accident detection, demonstrating the importance of incorporating motion cues. A comprehensive evaluation on a real-world dataset, alongside comparisons to leading technologies, validates the system’s performance. While the system accurately detects accidents, future work will focus on generating detailed descriptions of incidents, providing more comprehensive and informative results.
This research has significant implications for improving traffic monitoring, reducing emergency response times, and enhancing road safety. The technology could also be integrated into autonomous driving systems to improve their reliability and safety. In summary, this work presents a promising approach to road accident detection, demonstrating the effectiveness of a hybrid CNN-Transformer architecture and motion cue integration.
Transformer Networks Detect Traffic Accidents Robustly Scientists have developed a robust accident detection system for traffic surveillance, overcoming limitations in existing computer vision methods. Utilizing a comprehensive and balanced dataset, capturing diverse traffic environments and accident types, they proposed an accident detection model based on Transformer architecture, leveraging pre-extracted spatial video features to improve performance. The system employs convolutional layers to extract local correlations within each video frame, identifying patterns crucial for accurate analysis. These extracted features are then processed by Transformers, which capture sequential temporal dependencies, enabling the model to understand how events unfold over time. Recognizing that motion cues are essential for understanding dynamic scenes, the team rigorously evaluated multiple methods for incorporating this information. Experiments revealed that concatenating RGB features with optical flow achieved the highest accuracy, reaching 88. 3% in detecting accidents. Optical flow analysis provides detailed information about the movement of objects within the video, enhancing the model’s ability to discern abnormal events. To further validate the effectiveness of their approach, scientists compared the performance of their Transformer-based model with several vision language models, including GPT, Gemini, and LLaVA-NeXT-Video. This comparative analysis demonstrated the superior performance of the proposed method in accurately identifying traffic accidents, highlighting its potential for improving traffic surveillance and emergency response systems. The innovative integration of Transformer architecture, convolutional layers, and optical flow analysis represents a significant advancement in automated accident detection technology.
Transformer Model Detects Traffic Accidents Accurately Scientists have achieved a significant breakthrough in automated traffic accident detection by developing a novel Transformer-based model and a comprehensive dataset. Recognizing that current systems struggle with understanding spatiotemporal information, the team curated a balanced dataset encompassing diverse traffic environments and accident types to enable robust model training. The proposed architecture utilizes convolutional layers to extract local spatial correlations within video frames, while Transformers capture sequential temporal dependencies, improving the system’s ability to understand dynamic scenes. A key innovation of this work involved explicitly integrating motion cues, often overlooked in previous research, to better capture the unfolding of accident events. Experiments evaluated multiple methods for incorporating motion, ultimately demonstrating that concatenating RGB features with optical flow achieved the highest accuracy at 88. 3%. This result confirms the importance of motion information for accurately interpreting accident progression and significantly outperforms approaches relying solely on static features.
The team rigorously tested the effectiveness of their method by comparing its performance against several vision language models, including GPT, Gemini, and LLaVA-NeXT-Video, further validating the advancements achieved through the proposed architecture and data curation.
This research delivers a substantial improvement in automated accident detection, paving the way for more effective traffic surveillance systems and potentially reducing response times to critical incidents.
Traffic Accident Detection Using Spatiotemporal Networks This research presents a novel framework for detecting road traffic accidents through surveillance systems, addressing limitations in existing methods.
The team developed a model integrating convolutional neural networks to extract spatial features with transformer architectures to correlate temporal information, achieving an accuracy of 88. 3% on a newly curated, balanced dataset of diverse traffic scenarios. Crucially, the study demonstrates the benefit of explicitly incorporating motion cues, with the most effective approach involving the concatenation of RGB features with optical flow data. The findings represent a significant advancement in automated accident detection, potentially improving traffic monitoring and emergency response times. By benchmarking against large vision language models, researchers confirmed the effectiveness of their hybrid approach. The authors acknowledge current limitations and plan to address this through future work focused on generating comprehensive accident descriptions, aiming for more reliable and informative outcomes. 👉 More information 🗞 Surveillance Video-Based Traffic Accident Detection Using Transformer Architecture 🧠 ArXiv: https://arxiv.org/abs/2512.11350 Tags: