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

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