Predicting Pedestrian Crosswalk Behavior Using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2208.07250v1
• Date: Mon, 8 Aug 2022 22:48:22 GMT
• Title: Predicting Pedestrian Crosswalk Behavior Using Convolutional Neural Networks
• Authors: Eric Liang and Mark Stamp
• Abstract summary: Pedestrian accidents contribute a significant amount to the high number of annual traffic casualties. People often forget to activate a crosswalk light or are unable to do so. In this paper, we consider an improvement to the crosswalk system by designing a system that can detect pedestrians.
• Score: 7.227853810310584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A common yet potentially dangerous task is the act of crossing the street. Pedestrian accidents contribute a significant amount to the high number of annual traffic casualties, which is why it is crucial for pedestrians to use safety measures such as a crosswalk. However, people often forget to activate a crosswalk light or are unable to do so -- such as those who are visually impaired or have occupied hands. Other pedestrians are simply careless and find the crosswalk signals a hassle, which can result in an accident where a car hits them. In this paper, we consider an improvement to the crosswalk system by designing a system that can detect pedestrians and triggering the crosswalk signal automatically. We collect a dataset of images that we then use to train a convolutional neural network to distinguish between pedestrians (including bicycle riders) and various false alarms. The resulting system can capture and evaluate images in real time, and the result can be used to automatically activate systems a crosswalk light. After extensive testing of our system in real-world environments, we conclude that it is feasible as a back-up system that can compliment existing crosswalk buttons, and thereby improve the overall safety of crossing the street.

Related papers

• Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings [3.373568134827475]
  We propose and evaluate machine learning models to predict gap selection in non-zebra scenarios and zebra crossing usage in zebra scenarios. We discuss how pedestrians' behaviors are influenced by various factors, including pedestrian waiting time, walking speed, the number of unused gaps, the largest missed gap, and the influence of other pedestrians.
  arXiv  Detail & Related papers  (2024-04-15T08:36:40Z)
• Pedestrian Environment Model for Automated Driving [54.16257759472116]
  We propose an environment model that includes the position of the pedestrians as well as their pose information. We extract the skeletal information with a neural network human pose estimator from the image. To obtain the 3D information of the position, we aggregate the data from consecutive frames in conjunction with the vehicle position.
  arXiv  Detail & Related papers  (2023-08-17T16:10:58Z)
• Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis [0.0]
  Current methods for pedestrian collision prevention focus on integrating visual pedestrian detectors with Automatic Emergency Braking systems. Our system addresses such issues using an online, map-based pedestrian detection aggregation system. We demonstrate the system's ability to learn pedestrian zones and generate advisory notices when a vehicle is approaching a pedestrian.
  arXiv  Detail & Related papers  (2023-05-08T07:03:26Z)
• APE: An Open and Shared Annotated Dataset for Learning Urban Pedestrian Path Networks [16.675093530600154]
  Inferring the full transportation network, including sidewalks and cycleways, is crucial for many automated systems. This work begins to address this problem at scale by introducing a novel dataset of aerial satellite imagery, map imagery, and annotations of sidewalks, crossings, and corner bulbs in urban cities. We present an end-to-end process for inferring a connected pedestrian path network map using street network information and our proposed dataset.
  arXiv  Detail & Related papers  (2023-03-04T05:08:36Z)
• CHAMP: Crowdsourced, History-Based Advisory of Mapped Pedestrians for Safer Driver Assistance Systems [0.0]
  CHAMP (Crowdsourced, History-based Advisories of Mapped Pedestrians) learns pedestrian zones and generates advisory notices when a vehicle is approaching a pedestrian. We collect and carefully annotated pedestrian data in La Jolla, CA to construct training and test sets of pedestrian locations. This approach can be tuned such that we achieve a maximum of 100% precision and 75% recall on the experimental dataset.
  arXiv  Detail & Related papers  (2023-01-14T07:28:05Z)
• STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes [78.95447086305381]
  Accurately detecting and tracking pedestrians in 3D space is challenging due to large variations in rotations, poses and scales. Existing benchmarks either only provide 2D annotations, or have limited 3D annotations with low-density pedestrian distribution. We introduce a large-scale multimodal dataset, STCrowd, to better evaluate pedestrian perception algorithms in crowded scenarios.
  arXiv  Detail & Related papers  (2022-04-03T08:26:07Z)
• DR.VIC: Decomposition and Reasoning for Video Individual Counting [93.12166351940242]
  We propose to conduct pedestrian counting from a new perspective - Video Individual Counting (VIC) Instead of relying on the Multiple Object Tracking (MOT) techniques, we propose to solve the problem by decomposing all pedestrians into the initial pedestrians who existed in the first frame and the new pedestrians with separate identities in each following frame. An end-to-end Decomposition and Reasoning Network (DRNet) is designed to predict the initial pedestrian count with the density estimation method and reason the new pedestrian's count of each frame with the differentiable optimal transport.
  arXiv  Detail & Related papers  (2022-03-23T11:24:44Z)
• Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion [87.77727495366702]
  We introduce the new task of pedestrian stop and go forecasting. Considering the lack of suitable existing datasets for it, we release TRANS, a benchmark for explicitly studying the stop and go behaviors of pedestrians in urban traffic. We build it from several existing datasets annotated with pedestrians' walking motions, in order to have various scenarios and behaviors.
  arXiv  Detail & Related papers  (2022-03-04T18:39:31Z)
• Automated Object Behavioral Feature Extraction for Potential Risk Analysis based on Video Sensor [6.291501119156943]
  Pedestrians are exposed to risk of death or serious injuries on roads, especially unsignalized crosswalks. We propose an automated and simpler system for effectively extracting object behavioral features from video sensors deployed on the road. This study demonstrates the potential for a network of connected video sensors to provide actionable data for smart cities.
  arXiv  Detail & Related papers  (2021-07-08T01:11:31Z)
• End-to-End Deep Structured Models for Drawing Crosswalks [98.9901717499058]
  We project both inputs onto the ground surface to produce a top down view of the scene. We then leverage convolutional neural networks to extract semantic cues about the location of the crosswalks. Experiments over crosswalks in a large city area show that 96.6% automation can be achieved.
  arXiv  Detail & Related papers  (2020-12-21T18:59:08Z)
• Pedestrian Intention Prediction: A Multi-task Perspective [83.7135926821794]
  In order to be globally deployed, autonomous cars must guarantee the safety of pedestrians. This work tries to solve this problem by jointly predicting the intention and visual states of pedestrians. The method is a recurrent neural network in a multi-task learning approach.
  arXiv  Detail & Related papers  (2020-10-20T13:42:31Z)

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.