A Coarse-to-Fine Boundary Localization Method for Naturalistic Driving Action Recognition

Cited 4|Views37

Abstract

Naturalistic driving action recognition plays an important role in understanding drivers’ distracted behaviors in the traffic environment. The main challenge of this task is the accurate localization of the temporal boundary for each distracted driving behavior in the video. Although many temporal action localization methods can identify action categories, it is difficult to predict accurate temporal boundaries for this task since the driving actions of the same category usually present large intra-class variation. In this paper, we introduce a Coarse-to-Fine Boundary Localization method called CFBL, which obtains fine-grained temporal boundaries progressively through three stages. Concretely, in the first coarse boundary generation stage, we adopt a modified anchor-free model Anchor-Free Saliency-based Detector (AFSD) to make an interval estimation of the temporal boundaries of distracted behaviors. In the second boundary refinement stage, we use the Dense Boundary Generation (DBG) model to adjust the estimated interval of the temporal boundaries. In the final boundary decision stage, we build a Localization Boundary Refinement Module to determine the final boundaries of different actions. Besides, we adopt a voting strategy to combine the results of different camera views to enhance the model’s distracted driving action classification ability. The experiments conducted on the Track 3 validation set of the 2022 AI City Challenge demonstrate competitive performance of the proposed method.

Translated text

Key words

naturalistic driving action recognition,temporal boundary,distracted driving behavior,temporal action localization methods,action categories,accurate temporal boundaries,driving actions,fine-grained temporal boundaries,coarse boundary generation stage,modified anchor-free model Anchor,distracted behaviors,boundary refinement stage,final boundary decision stage,final boundaries,coarse-to-fine boundary localization method,dense boundary generation model,CFBL,localization boundary refinement module,drivers distracted behaviors

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper