Predicting Pedestrian Behavior at Zebra Crossings using Bottom-up Pose Estimation and Deep Learning
Keywords:Pedestrians’ pose estimation, behavioral analysis, Advanced Driver Assistance Systems, autonomous vehicles, Pedestrian Behavior classification..
Anticipating pedestrian behavior is critical for traffic management, developing Advanced Driver Assistance Systems (ADAS), and creating autonomous vehicles. However, the unpredictability of pedestrians at zebra crossings poses a significant challenge in designing systems that can aid drivers or enable self-driving. Existing studies often overlook pedestrian behavior and intentions when predicting motion, and there is no integrated system that connects perception and decision-making tasks. To address these challenges, we propose a new bottom-up pedestrian Pose Estimation model based on a CNN network that is trained with the deep learning VGG-19 Pretrained model. This model allows for the analysis of videos captured at zebra crossings and enables the detection and classification of pedestrian poses and movements such as walking, standing, hand signals, crossing, and not crossing. We train and evaluate our models on the pedestrian intention estimation (PIE) dataset using the COCO-18 key point model. Our approach provides a comprehensive solution for predicting pedestrian behavior at zebra crossings. Machine learning-based classifiers are used to compare classification performance across different prediction horizon values, resulting in improved accuracy and efficiency. Our proposed solution has significant implications for traffic management, ADAS, and autonomous vehicles, as it enables them to better anticipate and respond to pedestrian actions. Overall, this study highlights the importance of integrating perception and decision-making tasks in predicting pedestrian behavior and provides a promising solution for addressing this critical problem.
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