A Real-Time Predictive Pedestrian Collision Warning Service for Cooperative Intelligent Transportation Systems Using 3D Pose Estimation

• URL: http://arxiv.org/abs/2009.10868v4
• Date: Tue, 22 Feb 2022 03:40:11 GMT
• Title: A Real-Time Predictive Pedestrian Collision Warning Service for Cooperative Intelligent Transportation Systems Using 3D Pose Estimation
• Authors: Ue-Hwan Kim, Dongho Ka, Hwasoo Yeo, Jong-Hwan Kim
• Abstract summary: We propose a real-time predictive pedestrian collision warning service (P2CWS) for two tasks: pedestrian orientation recognition (100.53 FPS) and intention prediction (35.76 FPS) Our framework obtains satisfying generalization over multiple sites because of the proposed site-independent features. The proposed vision framework realizes 89.3% accuracy in the behavior recognition task on the TUD dataset without any training process.
• Score: 10.652350454373531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Minimizing traffic accidents between vehicles and pedestrians is one of the primary research goals in intelligent transportation systems. To achieve the goal, pedestrian orientation recognition and prediction of pedestrian's crossing or not-crossing intention play a central role. Contemporary approaches do not guarantee satisfactory performance due to limited field-of-view, lack of generalization, and high computational complexity. To overcome these limitations, we propose a real-time predictive pedestrian collision warning service (P2CWS) for two tasks: pedestrian orientation recognition (100.53 FPS) and intention prediction (35.76 FPS). Our framework obtains satisfying generalization over multiple sites because of the proposed site-independent features. At the center of the feature extraction lies 3D pose estimation. The 3D pose analysis enables robust and accurate recognition of pedestrian orientations and prediction of intentions over multiple sites. The proposed vision framework realizes 89.3% accuracy in the behavior recognition task on the TUD dataset without any training process and 91.28% accuracy in intention prediction on our dataset achieving new state-of-the-art performance. To contribute to the corresponding research community, we make our source codes public which are available at https://github.com/Uehwan/VisionForPedestrian

Related papers

• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints [25.550524178542833]
  We propose a novel multi-task learning framework for pedestrian crossing action recognition and trajectory prediction. We use 3D human keypoints extracted from raw sensor data to capture rich information on human pose and activity. We show that our approach achieves state-of-the-art performance on a wide range of evaluation metrics.
  arXiv  Detail & Related papers  (2023-06-01T18:27:48Z)
• Context-empowered Visual Attention Prediction in Pedestrian Scenarios [0.0]
  We present Context-SalNET, a novel encoder-decoder architecture that addresses three key challenges of visual attention prediction in pedestrians. First, Context-SalNET explicitly models the context factors urgency and safety preference in the latent space of the encoder-decoder model. Second, we propose the exponentially weighted mean squared error loss (ew-MSE) that is able to better cope with the fact that only a small part of the ground truth saliency maps consist of non-zero entries.
  arXiv  Detail & Related papers  (2022-10-30T19:38:17Z)
• Pedestrian 3D Bounding Box Prediction [83.7135926821794]
  We focus on 3D bounding boxes, which are reasonable estimates of humans without modeling complex motion details for autonomous vehicles. We suggest this new problem and present a simple yet effective model for pedestrians' 3D bounding box prediction. This method follows an encoder-decoder architecture based on recurrent neural networks.
  arXiv  Detail & Related papers  (2022-06-28T17:59:45Z)
• Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting [91.69900691029908]
  We advocate for predicting both the individual motions as well as the scene occupancy map. We propose a Scene-Actor Graph Neural Network (SA-GNN) which preserves the relative spatial information of pedestrians. On two large-scale real-world datasets, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods.
  arXiv  Detail & Related papers  (2021-01-07T06:08:21Z)
• 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)
• PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D [10.580548257913843]
  We propose a new pedestrian action prediction dataset created by adding per-frame 2D/3D bounding box and behavioral annotations to nuScenes. In addition, we propose a hybrid neural network architecture that incorporates various data modalities for predicting pedestrian crossing action.
  arXiv  Detail & Related papers  (2020-12-14T18:13:44Z)
• Recognition and 3D Localization of Pedestrian Actions from Monocular Video [11.29865843123467]
  This paper focuses on monocular pedestrian action recognition and 3D localization from an egocentric view. A challenge in addressing this problem in urban traffic scenes is attributed to the unpredictable behavior of pedestrians.
  arXiv  Detail & Related papers  (2020-08-03T19:57:03Z)
• STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction [24.855059537779294]
  We present a novel end-to-end two-stage network: Spatio--Interactive Network (STINet) In addition to 3D geometry of pedestrians, we model temporal information for each of the pedestrians. Our method predicts both current and past locations in the first stage, so that each pedestrian can be linked across frames.
  arXiv  Detail & Related papers  (2020-05-08T18:43:01Z)
• Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction [57.56466850377598]
  Reasoning over visual data is a desirable capability for robotics and vision-based applications. In this paper, we present a framework on graph to uncover relationships in different objects in the scene for reasoning about pedestrian intent. Pedestrian intent, defined as the future action of crossing or not-crossing the street, is a very crucial piece of information for autonomous vehicles.
  arXiv  Detail & Related papers  (2020-02-20T18:50:44Z)

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.