Pedestrian intention recognition by means of a Hidden Markov Model and body language

Quintero, R.; Parra, I.; Lorenzo, Javier; Llorca, David Fernández; Sotelo, Miguel Ángel

doi:10.1109/itsc.2017.8317766

Cited by 59 publications

(27 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Deep Learning For Intention Estimationmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Quintero et al [8] used Balanced Gaussian Process Dynamical Models and a naïve-Bayes classifier for intention and pose prediction of pedestrians based on 3D joint positions. This approach was extended by a Hidden Markov Model in [5]. They reached an accuracy of 95.13% for intention detection and were able to detect starting motions 0.125 s after gait initiation with an accuracy of 80% on a high frequency and low noise dataset.…”

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)

View full text Add to dashboard Cite

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,

show abstract

“…Pedestrian-only approaches [1], [3], [4], [7], [10], [11], in general, learn machine learning models over pedestrian features (motion and/or appearance) and n-ary intention labels. Pedestrian features include dense optical flow [1], concatenated position, velocity and head pose [3], Motion Contour image based Histogram of Oriented Gradients descriptor (MCHOG) [4], skeletal features [7], [10] and deep learning appearance features [11]. Applied machine learning have been either time-series models (GPDM, PHTM [1], LDCRF [3], HMM [7], LSTM [11]) or SVM [4], [10].…”

Section: A Pedestrian Intention Predictionmentioning

confidence: 99%

Context based pedestrian intention prediction using factored latent dynamic conditional random fields

Neogi¹,

Hoy²,

Weng³

et al. 2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

View full text Add to dashboard Cite

Smooth handling of pedestrian interactions is a key requirement for Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS). Such systems call for early and accurate prediction of a pedestrian's crossing/not-crossing behaviour in front of the vehicle. Existing approaches to pedestrian behaviour prediction make use of pedestrian motion, his/her location in a scene and static context variables such as traffic lights, zebra crossings etc. We stress on the necessity of early prediction for smooth operation of such systems. We introduce the influence of vehicle interactions on pedestrian intention for this purpose. In this paper, we show a discernible advance in prediction time aided by the inclusion of such vehicle interaction context. We apply our methods to two different datasets, one in-house collected -NTU dataset and another public real-life benchmark -JAAD dataset. We also propose a generic graphical model Factored Latent-Dynamic Conditional Random Fields (FLDCRF) for single and multilabel sequence prediction as well as joint interaction modeling tasks. FLDCRF outperforms Long Short-Term Memory (LSTM) networks across the datasets (∼100 sequences per dataset) over identical time-series features. While the existing best system predicts pedestrian stopping behaviour with 70% accuracy 0.38 seconds before the actual events, our system achieves such accuracy at least 0.9 seconds on an average before the actual events across datasets.

show abstract

Pedestrian intention recognition by means of a Hidden Markov Model and body language

Cited by 59 publications

References 10 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

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

Context based pedestrian intention prediction using factored latent dynamic conditional random fields

Contact Info

Product

Resources

About