Towards Infusing Auxiliary Knowledge for Distracted Driver Detection

• URL: http://arxiv.org/abs/2408.16621v1
• Date: Thu, 29 Aug 2024 15:28:42 GMT
• Title: Towards Infusing Auxiliary Knowledge for Distracted Driver Detection
• Authors: Ishwar B Balappanawar, Ashmit Chamoli, Ruwan Wickramarachchi, Aditya Mishra, Ponnurangam Kumaraguru, Amit P. Sheth,
• Abstract summary: Distracted driving is a leading cause of road accidents globally. We propose KiD3, a novel method for distracted driver detection (DDD) by infusing auxiliary knowledge about semantic relations between entities in a scene and the structural configuration of the driver's pose. Specifically, we construct a unified framework that integrates the scene graphs, and driver pose information with the visual cues in video frames to create a holistic representation of the driver's actions.
• Score: 11.816566371802802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distracted driving is a leading cause of road accidents globally. Identification of distracted driving involves reliably detecting and classifying various forms of driver distraction (e.g., texting, eating, or using in-car devices) from in-vehicle camera feeds to enhance road safety. This task is challenging due to the need for robust models that can generalize to a diverse set of driver behaviors without requiring extensive annotated datasets. In this paper, we propose KiD3, a novel method for distracted driver detection (DDD) by infusing auxiliary knowledge about semantic relations between entities in a scene and the structural configuration of the driver's pose. Specifically, we construct a unified framework that integrates the scene graphs, and driver pose information with the visual cues in video frames to create a holistic representation of the driver's actions.Our results indicate that KiD3 achieves a 13.64% accuracy improvement over the vision-only baseline by incorporating such auxiliary knowledge with visual information.

Related papers

• Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning [13.613407983544427]
  We introduce a robust model designed to withstand changes in camera position within the vehicle. Our Driver Behavior Monitoring Network (DBMNet) relies on a lightweight backbone and integrates a disentanglement module. Experiments conducted on the daytime and nighttime subsets of the 100-Driver dataset validate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-11-20T10:27:12Z)
• Enhancing Road Safety: Real-Time Detection of Driver Distraction through Convolutional Neural Networks [0.0]
  This study seeks to identify the most efficient model for real-time detection of driver distractions. The ultimate aim is to incorporate the findings into vehicle safety systems, significantly boosting their capability to prevent accidents triggered by inattention.
  arXiv  Detail & Related papers  (2024-05-28T03:34:55Z)
• PoseViNet: Distracted Driver Action Recognition Framework Using Multi-View Pose Estimation and Vision Transformer [1.319058156672392]
  This paper introduces a novel method for detection of driver distraction using multi-view driver action images. The proposed method is a vision transformer-based framework with pose estimation and action inference, namely PoseViNet. The PoseViNet achieves 97.55% validation accuracy and 90.92% testing accuracy with the challenging dataset.
  arXiv  Detail & Related papers  (2023-12-22T10:13:10Z)
• OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping [84.65114565766596]
  We present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.
  arXiv  Detail & Related papers  (2023-04-20T16:31:22Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception [59.2014692323323]
  Small, far-away, or highly occluded objects are particularly challenging because there is limited information in the LiDAR point clouds for detecting them. We propose a novel, end-to-end trainable Hindsight framework to extract contextual information from past data. We show that this framework is compatible with most modern 3D detection architectures and can substantially improve their average precision on multiple autonomous driving datasets.
  arXiv  Detail & Related papers  (2022-03-22T00:58:27Z)
• Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection [55.12894776039135]
  State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit to domain idiosyncrasies. We propose a novel learning approach that drastically reduces this gap by fine-tuning the detector on pseudo-labels in the target domain. We show, on five autonomous driving datasets, that fine-tuning the detector on these pseudo-labels substantially reduces the domain gap to new driving environments.
  arXiv  Detail & Related papers  (2021-03-26T01:18:11Z)
• The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention [44.94118128276982]
  A smart vehicle should be able to monitor the actions and behaviors of the human driver to provide critical warnings or intervene when necessary. Recent advancements in deep learning and computer vision have shown great promise in monitoring human behaviors and activities. A vast amount of in-domain data is required to train models that provide high performance in predicting driving related tasks.
  arXiv  Detail & Related papers  (2020-12-23T16:37:17Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• Driver Glance Classification In-the-wild: Towards Generalization Across Domains and Subjects [5.562102367018285]
  Driver assistance systems (ADAS) with the ability to detect driver distraction can help prevent accidents and improve driver safety. We propose a model that takes as input a patch of the driver's face along with a crop of the eye-region and classifies their glance into 6 coarse regions-of-interest (ROIs) in the vehicle.
  arXiv  Detail & Related papers  (2020-12-05T00:23:01Z)
• Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring [52.557003792696484]
  We present a framework for the detection of the drivers' intention based on both in-cabin and traffic scene videos. Our framework achieves a prediction with the accuracy of 83.98% and F1-score of 84.3%.
  arXiv  Detail & Related papers  (2020-06-20T11:56:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.