Stereo-Vision-Based Pedestrian's Intention Detection in a Moving Vehicle

Köhler, Sebastian; Goldhammer, Michael; Zindler, Klaus; Doll, Konrad; Dietmeyer, Klaus

doi:10.1109/itsc.2015.374

Cited by 37 publications

(45 citation statements)

References 20 publications

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“…DL has been used for intention estimation [186,187] with promising results as will be discussed in this section. Some of the literature for intent estimation can be found in [178,183,[188][189][190][191][192][193][194][195][196][197][198] which is summarised in Table 6. The models were applied for four pedestrian motions; crossing, stopping, bending and starting.…”

Section: Deep Learning For Intention Estimationmentioning

confidence: 99%

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

et al. 2019

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As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated.

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Section: Deep Learning For Intention Estimationmentioning

confidence: 99%

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

et al. 2019

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“…In the first experiment, the quality of the proposed method for motion state classification is evaluated. For comparison, an IMM-KF using CP/CV models and the directly video-based method MCHOG/SVM [23] are applied to the same scenes. As the evaluation requires the additional labels of the Detailed Pedestrian Dataset (i.e.…”

Section: B Motion State Classificationmentioning

confidence: 99%

Intentions of Vulnerable Road Users—Detection and Forecasting by Means of Machine Learning

Goldhammer

Köhler

Zernetsch

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Avoiding collisions with vulnerable road users (VRUs) using sensor-based early recognition of critical situations is one of the manifold opportunities provided by the current development in the field of intelligent vehicles. As especially pedestrians and cyclists are very agile and have a variety of movement options, modeling their behavior in traffic scenes is a challenging task. In this article we propose movement models based on machine learning methods, in particular artificial neural networks, in order to classify the current motion state and to predict the future trajectory of VRUs. Both model types are also combined to enable the application of specifically trained motion predictors based on a continuously updated pseudo probabilistic state classification. Furthermore, the architecture is used to evaluate motion-specific physical models for starting and stopping and video-based pedestrian motion classification. A comprehensive dataset consisting of 1068 pedestrian and 494 cyclist scenes acquired at an urban intersection is used for optimization, training, and evaluation of the different models. The results show substantial higher classification rates and the ability to earlier recognize motion state changes with the machine learning approaches compared to interacting multiple model (IMM) Kalman Filtering. The trajectory prediction quality is also improved for all kinds of test scenes, especially when starting and stopping motions are included. Here, 37% and 41% lower position errors were achieved on average, respectively.

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“…Overall, an accuracy of 99% for starting detection was reached within the first step of the pedestrian. In [7], this method was transformed for usage in a moving vehicle and extended by stopping and bending in intentions.…”

Section: B Related Workmentioning

confidence: 99%

Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network

Zernetsch

Kress

Sick

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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In this article, we present a novel approach to detect starting motions of cyclists in real world traffic scenarios based on Motion History Images (MHIs). The method uses a deep Convolutional Neural Network (CNN) with a residual network architecture (ResNet), which is commonly used in image classification and detection tasks. By combining MHIs with a ResNet classifier and performing a frame by frame classification of the MHIs, we are able to detect starting motions in image sequences. The detection is performed using a wide angle stereo camera system at an urban intersection. We compare our algorithm to an existing method to detect movement transitions of pedestrians that uses MHIs in combination with a Histograms of Oriented Gradients (HOG) like descriptor and a Support Vector Machine (SVM), which we adapted to cyclists. To train and evaluate the methods a dataset containing MHIs of 394 cyclist starting motions was created. The results show that both methods can be used to detect starting motions of cyclists. Using the SVM approach, we were able to safely detect starting motions 0.506 s on average after the bicycle starts moving with an F 1 -score of 97.7%. The ResNet approach achieved an F 1score of 100% at an average detection time of 0.144 s. The ResNet approach outperformed the SVM approach in both robustness against false positive detections and detection time.S. Zernetsch, V. Kress and K. Doll are with the Faculty of Engineering, University of Applied Sciences Aschaffenburg,

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Stereo-Vision-Based Pedestrian's Intention Detection in a Moving Vehicle

Cited by 37 publications

References 20 publications

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

Intentions of Vulnerable Road Users—Detection and Forecasting by Means of Machine Learning

Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network

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