Abstract
Road traffic accidents represent a leading cause of mortality globally, with incidence rates rising due to increasing population, urbanization, and motorization. Rising accident rates raise concerns about traffic surveillance effectiveness. Traditional computer vision methods for accident detection struggle with limited spatiotemporal understanding and poor cross-domain generalization. Recent advances in transformer architectures excel at modeling global spatial-temporal dependencies and parallel computation. However, applying these models to automated traffic accident detection is limited by small, non-diverse datasets, hindering the development of robust, generalizable systems. To address this gap, we curated a comprehensive and balanced dataset that captures a wide spectrum of traffic environments, accident types, and contextual variations. Utilizing the curated dataset, we propose an accident detection model based on a transformer architecture using pre-extracted spatial video features. The architecture employs convolutional layers to extract local correlations across diverse patterns within a frame, while leveraging transformers to capture sequential-temporal dependencies among the retrieved features. Moreover, most existing studies neglect the integration of motion cues, which are essential for understanding dynamic scenes, especially during accidents. These approaches typically rely on static features or coarse temporal information. In this study, multiple methods for incorporating motion cues were evaluated to identify the most effective strategy. Among the tested input approaches, concatenating RGB features with optical flow achieved the highest accuracy at 88.3%. The results were further compared with vision language models (VLM) such as GPT, Gemini, and LLaVA-NeXT-Video to assess the effectiveness of the proposed method.
Abstract (translated)
道路交通事故是全球死亡的主要原因之一，由于人口增长、城市化和机动化的加剧，事故发生率正在上升。这一趋势引发了人们对交通监控有效性的担忧。传统的计算机视觉方法在事故检测方面面临着时空理解有限以及跨领域泛化能力差的问题。最近，基于变换器架构的进展在建模全局空间-时间依赖性和并行计算方面表现出色。然而，在自动交通事故检测中应用这些模型受到小规模、非多样化的数据集的限制，这阻碍了开发稳健且通用系统的进程。 为了解决这一问题，我们整理了一个全面而平衡的数据集，该数据集涵盖了广泛的交通环境、事故类型和情境变化。利用这个精心策划的数据集，我们提出了一种基于变换器架构并使用预提取空间视频特征的事故检测模型。这种架构通过卷积层从帧内各种模式中抽取局部相关性，并利用变换器捕捉所检索到的特性之间的序列-时间依赖关系。 此外，大多数现有研究忽略了运动线索（对理解动态场景尤其是事故发生时至关重要的信息）的整合。这些方法通常依赖于静态特征或粗略的时间信息。在本研究中，我们评估了多种包含运动线索的方法以确定最有效的策略。在测试的输入方式中，并联RGB特性与光流获得最高的准确性，达到了88.3%。 我们的结果还与其他视觉语言模型（如GPT、Gemini和LLaVA-NeXT-Video）进行了对比，以评估所提出方法的有效性。
URL
https://arxiv.org/abs/2512.11350
