A Multi-Stream Approach to Mixed-Traffic Accident Recognition Using Deep Learning

被引：0
|
作者：
Fu, Swee Tee [1 ]
Theng, Lau Bee [1 ]
Shiong, Brian Loh Chung [1 ]
Mccarthy, Chris [2 ]
Tsun, Mark Tee Kit [1 ]
机构：
[1] Swinburne University of Technology, Faculty of Engineering, Computing and Science, Sarawak,Sarawak Campus, Kuching,93350, Malaysia
[2] Swinburne University of Technology, School of Science, Computing and Engineering Technologies (SoSCET), Melbourne Campus, Hawthorn,VIC,3122, Australia
来源：
IEEE Access | 2024年 / 12卷
关键词：
Road traffic accidents are a leading cause of injuries and fatalities globally; prompting extensive research into deep learning-based accident recognition models for their superior performance in computer vision tasks. However; most studies focus on non-mixed traffic environments; where detection is simpler due to predictable traffic patterns and uniform vehicle types. In contrast; mixed-traffic scenarios present greater challenges as diverse vehicles; motorcyclists; and pedestrians move unpredictably. Models relying on a single type of perception are effective in structured traffic but struggle to handle the complexities of mixed-traffic environments. This study proposes a novel multi-stream deep learning model called Accident Recognition in Mixed-Traffic Scene (ARMS); which integrates three distinct streams: the first stream analyzes the overall accident scene; the second focuses on mixed-traffic accident features; and the third examines vehicle motion abnormalities through object detection and tracking; aimed at improving road accident recognition accuracy in mixed-traffic environments at intersections. This model is trained and evaluated using datasets from CADP; UA-DETRAC; and supplementary online sources. The results demonstrate that the ARMS model achieves an accuracy of 93.3%; with performance improving significantly through the fusion of the individual streams. Additionally; the ARMS model was evaluated using two publicly available standard datasets; which further highlights its improved performance in recognizing mixed-traffic accidents compared to existing studies. © 2013 IEEE;
D O I：
10.1109/ACCESS.2024.3512794
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：185232 / 185249
相关论文
共 50 条
  • [21] A multi-stream approach to off-line handwritten word recognition
    Kessentini, Yousri
    Paquet, Thierry
    Benhamadou, AbdelMajid
    ICDAR 2007: NINTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS I AND II, PROCEEDINGS, 2007, : 317 - +
  • [22] Multi-stream with Deep Convolutional Neural Networks for Human Action Recognition in Videos
    Liu, Xiao
    Yang, Xudong
    NEURAL INFORMATION PROCESSING (ICONIP 2018), PT I, 2018, 11301 : 251 - 262
  • [23] A new multi-stream approach using acoustic and visual features for robust speech recognition system
    Radha, N.
    Shahina, A.
    Khan, A. Nayeemulla
    Velusami, Jansi Rani Sella
    MATERIALS TODAY-PROCEEDINGS, 2022, 62 : 4916 - 4924
  • [24] Gaze-Assisted Multi-Stream Deep Neural Network for Action Recognition
    Liu, Yinan
    Wu, Qingbo
    Tang, Liangzhi
    Shi, Hengcan
    IEEE ACCESS, 2017, 5 : 19432 - 19441
  • [25] DG-ECG: Multi-stream deep graph learning for the recognition of disease-altered patterns in electrocardiogram
    Kan, Chen
    Ye, Zehao
    Zhou, Houliang
    Cheruku, Sreekanth R.
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2023, 80
  • [26] A Multi-Stream Approach for Video Understanding
    Kunam, Lutharsanen
    Rossetto, Luca
    Bernstein, Abraham
    PROCEEDINGS OF THE 30TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2022, 2022, : 7003 - 7007
  • [27] Multi-stream parameterization for structural speech recognition
    Asakawa, Satoshi
    Minematsu, Nobuaki
    Hirose, Keikichi
    2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, 2008, : 4097 - +
  • [28] Can segregating vehicles in mixed-traffic stream improve safety and throughput? implications using simulation
    Paul, Geetashree
    Raju, Narayana
    Arkatkar, Shriniwas
    Easa, Said
    TRANSPORTMETRICA A-TRANSPORT SCIENCE, 2021, 17 (04) : 1002 - 1026
  • [29] Multimodal Gesture Recognition Using Multi-stream Recurrent Neural Network
    Nishida, Noriki
    Nakayama, Hideki
    IMAGE AND VIDEO TECHNOLOGY, PSIVT 2015, 2016, 9431 : 682 - 694
  • [30] Multi-stream mixed graph convolutional networks for skeleton-based action recognition
    Zhuang, Boyuan
    Kong, Jun
    Jiang, Min
    Liu, Tianshan
    JOURNAL OF ELECTRONIC IMAGING, 2021, 30 (06)
12345