Pose Based Start Intention Detection of Cyclists

Kress, Viktor; Jung, Janis; Zernetsch, Stefan; Doll, Konrad; Sick, Bernhard

doi:10.1109/itsc.2019.8917215

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…The proposed models have shown robust prediction results when evaluated on real-life cyclist trajectory datasets collected using vehicle-based sensors in the urban traffic environment. In [24] start intention is detected with CNN architecture and Motion History Images (MHIs) based on their dataset, the same problem is addressed in [25] training the system with a dataset of 3D poses of cyclists recorded from a moving vehicle in real road traffic. In another context-based path prediction for cyclists [26] , authors proposed a Recurrent Neural Network (RNN) to learn the effect of contextual cues directly on the behavior (one related to the actions of the cyclist, one related to the location of the cyclist on the road, and one related to the interaction between the cyclist and the ego-vehicle), then the RNN predicts a Gaussian distribution over the future position of the cyclist.…”

Section: Hardware In Contextmentioning

confidence: 99%

CYCLOPS: A cyclists’ orientation data acquisition system using RGB camera and inertial measurement units (IMU)

Arias-Correa,

Robledo,

Londoño

et al. 2024

HardwareX

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Section: Hardware In Contextmentioning

confidence: 99%

CYCLOPS: A cyclists’ orientation data acquisition system using RGB camera and inertial measurement units (IMU)

Arias-Correa,

Robledo,

Londoño

et al. 2024

HardwareX

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“…GPDM in combination with human poses extracted from image sequences to detect start intentions of pedestrians were introduced in [3] and [4]. In [5], the authors used 3D poses of cyclists to detect starting motions from a moving vehicle. In [6] and [7], an approach based on MHI in combination with a support vector machine (SVM) to detect pedestrians' starting and stopping intentions was presented.…”

Section: B Related Workmentioning

confidence: 99%

Cyclist Intention Detection: A Probabilistic Approach

Zernetsch¹,

Reichert²,

Kress³

et al. 2021

Preprint

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This article presents a holistic approach for probabilistic cyclist intention detection. A basic movement detection based on motion history images (MHI) and a residual convolutional neural network (ResNet) are used to estimate probabilities for the current cyclist motion state. These probabilities are used as weights in a probabilistic ensemble trajectory forecast. The ensemble consists of specialized models, which produce individual forecasts in the form of Gaussian distributions under the assumption of a certain motion state of the cyclist (e.g. cyclist is starting or turning left). By weighting the specialized models, we create forecasts in the from of Gaussian mixtures that define regions within which the cyclists will reside with a certain probability. To evaluate our method, we rate the reliability, sharpness, and positional accuracy of our forecasted distributions. We compare our method to a single model approach which produces forecasts in the form of Gaussian distributions and show that our method is able to produce more reliable and sharper outputs while retaining comparable positional accuracy. Both methods are evaluated using a dataset created at a public traffic intersection. Our code and the dataset are made publicly available.

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“…Todo lo anterior justifica el desarrollo de detectores de ciclistas urbanos, como usuarios vulnerables de la vía, para ser implementados en los vehículos autónomos [12].…”

Section: Introductionunclassified

Comparación de arquitecturas YOLO para la detección de ciclistas urbanos en un entorno de vehículos autónomos

Arias-Correa,

David Rodríguez,

Quintero Restrepo

et al. 2024

RCTA

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La OMS establece que más del 55% de las muertes en accidentes viales son de usuarios vulnerables, incluyendo un 3% de ciclistas. Aunque los vehículos autónomos pueden detectar objetos y personas en las carreteras, la detección de ciclistas y la predicción de sus movimientos siguen siendo desafíos significativos. Este artículo presenta resultados al comparar las arquitecturas YOLOv7, YOLOv8 y YOLO-NAS para detectar ciclistas urbanos. La metodología garantiza que los detectores se entrenaron bajo las mismas condiciones. Luego, se evaluaron con 111 imágenes de ciclistas utilizando métricas como IoU, precisión y recall. Los resultados destacan ventajas y desventajas en cada arquitectura, lo que sugiere priorizar el tiempo de inferencia o la calidad de la detección de ciclistas en futuros trabajos.

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Pose Based Start Intention Detection of Cyclists

Cited by 11 publications

References 16 publications

CYCLOPS: A cyclists’ orientation data acquisition system using RGB camera and inertial measurement units (IMU)

CYCLOPS: A cyclists’ orientation data acquisition system using RGB camera and inertial measurement units (IMU)

Cyclist Intention Detection: A Probabilistic Approach

Comparación de arquitecturas YOLO para la detección de ciclistas urbanos en un entorno de vehículos autónomos

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