temporal driver action Localization using action classifications method
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.
Code (1)
Tasks
Action ClassificationAction LocalizationTemporal Action LocalizationSimilar Papers 제목 키워드 기반
Stargazer: A transformer-based driver action detection system for intelligent transportation
Distracted driver actions can be dangerous and cause severe accidents. Thus, it is important to detect and eliminate distracted driving behaviors on the road to save lives. To this end, we study driver action detection u…
Action DetectionAction RecognitionTemporal LocalizationTransformer-based Fusion of 2D-pose and Spatio-temporal Embeddings for Distracted Driver Action Recognition
Classification and localization of driving actions over time is important for advanced driver-assistance systems and naturalistic driving studies. Temporal localization is challenging because it requires robustness, reli…
2D Human Pose EstimationAction RecognitionPose EstimationTemporal Action Localization+1A Two-stage Transformer Framework for Temporal Localization of Distracted Driver Behaviors
The identification of hazardous driving behaviors from in-cabin video streams is essential for enhancing road safety and supporting the detection of traffic violations and unsafe driver actions. However, current temporal…
Temporal Action LocalizationComputational EfficiencyExploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization
Weakly-supervised temporal action localization aims to localize actions in untrimmed videos with only video-level labels. Most existing methods address this problem with a "localization-by-classification" pipeline th…
Action LocalizationContrastive LearningDenoisingPseudo Label+3DeepLocalization: Using change point detection for Temporal Action Localization
In this study, we introduce DeepLocalization, an innovative framework devised for the real-time localization of actions tailored explicitly for monitoring driver behavior. Utilizing the power of advanced deep learning me…
Action LocalizationChange Point DetectionEvent DetectionLanguage Modeling+4