Spatial Attention Mechanism for Weakly Supervised Fire and Traffic Accident Scene Classification
Abstract
During the past ten years, on average there were near 16.5 thousands of hazardous materials (hazmat) transport incidents per year resulting in $82 millions of damages. Prompt, accurate, objective assessment on hazmat incidents is important for the first-responders to take appropriate actions timely, which will reduce the damage of hazmat incidents and protect the safety of people and the environment. Therefore, one of the most important steps is to automatically detect transport incidents, such as fire and traffic accidents. In this paper, we introduce a simple and yet effective framework that integrates the convolutional feature maps of deep Convolutional Neural Network with a spatial attention mechanism for fire and traffic accident scene classification. Our spatial attention model learns to highlight the most discriminative convolutional features, which is related to the regions of interest in the input image. We train our network in a weakly supervised way. In other words, without the requirement of precise bounding box annotating the exact location of fire or traffic accidents in the image, our network can be learned from the only image-level label. In addition to the image-based traffic scene classification, the model is also applied on a set of collected videos for real-world applications. The proposed model, a simple end-to-end architecture, achieves promising performance on fire scene classification from images, and traffic accident scene classification from both images and videos.
Recommended Citation
M. Moniruzzaman et al., "Spatial Attention Mechanism for Weakly Supervised Fire and Traffic Accident Scene Classification," Proceedings of the 5th IEEE International Conference on Smart Computing (2019, Washington, DC), Institute of Electrical and Electronics Engineers (IEEE), Jun 2019.
The definitive version is available at https://doi.org/10.1109/SMARTCOMP.2019.00061
Meeting Name
5th IEEE International Conference on Smart Computing, SMARTCOMP’19 (2019: Jun. 12-15, Washington, DC)
Department(s)
Computer Science
Second Department
Engineering Management and Systems Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Convolutional Neural Network; Spatial Attention; Weakly Supervised; Traffic Accidents
International Standard Book Number (ISBN)
978-1-7281-1689-1
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
15 Jun 2019
Comments
This work was supported by Mid-America Transportation Center (MATC), and Intelligent Systems Center (ISC) at Missouri University of Science and Technology.