temporal driver action Localization using action classifications method

Code

• github.com/Shahad24/AICITY2022_Track3_Team95
  pytorch★ 5

Abstract

Driver distraction recognition is an essential computer vision task that can play a key role in increasing traffic safety and reducing traffic accidents. In this paper, we propose a temporal driver action localization (TDAL) framework for classifying driver distraction actions, as well as identifying the start and end time of a given driver action. The TDAL framework consists of three stages: preprocessing, which takes untrimmed video as input and generates multiple clips; action classification, which classifies the clips; and finally, the classifier output is sent to the temporal action localization to generate the start and end times of the distracted actions. The proposed framework achieves an F1 score of 27.06% on Track 3 A2 dataset of NVIDIA AI City 2022 Challenge. The findings show that the TDAL framework contributes to fine-grained driver distraction recognition and paves the way for the development of smart and safe transportation.

Tasks

Reproductions