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Real Time 2D Pose Estimation for Pedestrian Path Estimation Using GPU Computing
Technical Paper
2019-01-0887
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Future fully autonomous and partially autonomous cars equipped with Advanced Driver Assistant Systems (ADAS) should assure safety for the pedestrian. One of the critical tasks is to determine if the pedestrian is crossing the road in the path of the ego-vehicle, in order to issue the required alerts for the driver or even safety breaking action. In this paper, we investigate the use of 2D pose estimators to determine the direction and speed of the pedestrian crossing the road in front of a vehicle. Pose estimation of body parts, such as right eye, left knee, right foot, etc… is used for determining the pedestrian orientation while tracking these key points between frames is used to determine the pedestrian speed. The pedestrian orientation and speed are the two required elements for the basic path estimation. High Performance Computing (HPC) has recently been considerably improved, for instance GPU Computing has been developed to solve complex problems and transform the Graphics Processing Unit (GPU) into a massively parallel processor. To enhance the performance of our pose estimator we limit the process to a region of interest (ROI) where the pedestrian is initially detected using a GPU accelerated detector.
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Abughalieh, K. and Alawneh, S., "Real Time 2D Pose Estimation for Pedestrian Path Estimation Using GPU Computing," SAE Technical Paper 2019-01-0887, 2019, https://doi.org/10.4271/2019-01-0887.Data Sets - Support Documents
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References
- Retting , R. and Schwartz , S. Pedestrian Traffic Fatalities by State: 2017 PRELIMINARY DATA 2018
- López , D.G.M. Vision-Based Pedestrian Protection Systems for Intelligent Vehicles Springer 2014
- Chen , G. , Ding , Y. , Xiao , J. and Han , T. X. Detection Evolution with Multi-Order Contextual Co-Occurrence Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2013
- Felzenszwalb , P.F. , Girshick , R.B. , McAllester , D. , and Ramanan , D. Object Detection with Discriminatively Trained Part-Based Models IEEE Transactions on Pattern Analysis and Machine Intelligence 32 9 1627 1645 2010
- Wang , X. , Han , T. X. , and Yan , S. An HOG-LBP Human Detector with Partial Occlusion Handling IEEE 12th International Conference on Computer Vision 2009
- Sermanet , P. , Kavukcuoglu , K. , Chintala , S. and LeCun , Y. Pedestrian Detection with Unsupervised Multi-Stage Feature Conference on Computer Vision and Pattern Recognition (CVPR) 2013
- Ouyang , W. and Wang , X. A Discriminative Deep Model for Pedestrian Detection with Occlusion Handling Conference on Computer Vision and Pattern Recognition 2012
- Ouyang , W. and Wang , X. Joint Deep Learning for Pedestrian Detection International Conference on Computer Vision 2013
- Andriluka , M. , Roth , S. , and Schiele , B. Monocular 3d Pose Estimation and Tracking by Detection Computer Vision and Pattern Recognition (CVPR) 2010
- Felzenszwalb , P.F. and Huttenlocher , D.P. Pictorial Structures for Object Recognition International Journal of Computer Vision 55 79 2005
- Ramakrishna V. , Munoz D. , Hebert M. , Bagnell J. A. , et al. Pose Machines: Articulated Pose Estimation via Inference Machines European Conference on Computer Vision 2010
- Cao , Z. , Simon , T. , Wei , S.-E. and Sheikh , Y. 2016
- Schulz , A.T. and Stiefelhagen , R. Pedestrian Intention Recognition Using Latent-Dynamic Conditional Random Fields Intelligent Vehicles Symposium (IV) Seoul, South Korea 2015
- Schulz , A.T. and Stiefelhagen , R. A Controlled Interactive Multiple Model Filter for Combined Pedestrian Intention Recognition and Path Prediction 18th International Conference on Intelligent Transportation Systems Las Palmas, Spain 2015
- Møgelmose , A. , Trivedi , M.M. , and Moeslund , T.B. Trajectory Analysis and Prediction for Improved Pedestrian Safety: Integrated Framework and Evaluations IEEE Intelligent Vehicles Symposium (IV) 2015
- Kooij , J.F.P. , Schneider , N. , Flohr , F. , and Gavrila , D.M. Context-Based Pedestrian Path Prediction European Conference on Computer Vision
- Quintero , R. , Parra , I. , Llorca , D.F. , and Sotelo , M.A. Pedestrian Path Prediction Based on Body Language and Action Classification Intelligent Transportation Systems (ITSC) 2014
- Pellegrini , S. , Ess , A. , Schindler , K. , and Gool , L.V. You'll Never Walk Alone: Modeling Social Behavior for Multi-Target Tracking Modeling Social Behavior for Multi-Target Tracking. In Computer Vision 2009
- Kataoka , H. , Aoki , Y. , Satoh , Y. , Oikawa , S. et al. Fine-Grained Walking Activity Recognition via Driving Recorder Dataset Intelligent Transportation Systems (ITSC) 2015
- Choi , W. and Savarese , S. Understanding Collective Activities of People from Videos IEEE Transactions on Pattern Analysis and Machine Intelligence 36 6 1242 1257 2014
- Gkioxari , G. , Hariharan , B. , Girshick , R. , and Malik , J. Using K-Poselets for Detecting People and Localizing Their Keypoints Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2014
- Pishchulin , L. , Jain , A. , Andriluka , M. , Thormählen , T. et al. Articulated People Detection and Pose Estimation: Reshaping the Future Computer Vision and Pattern Recognition (CVPR) 2012
- Kim , I. TF Pose-Estimation GitHub 2018
- Wei , S.-E. , Ramakrishna , V. , Kanade , T. , and Sheikh , Y. Convolutional Pose Machines Conference on Computer Vision and Pattern Recognition 2016
- Jia , Y. , Shelhamer , E. , Donahue , J. , Karayev , S. et al. Caffe: Convolutional Architecture for Fast Feature Embedding Proceedings of the 22nd ACM International Conference on Multimedia
- Howard , A. G. , Zhu , M. , Chen , B. , Kalenichenko , D. et al. 2017
- Tome , D. , Russell , C. , and Agapito , L. Lifting from the Deep: Convolutional 3d Pose Estimation from a Single Image CVPR 2017 Proceedings 2017 2500 2509
- Dominguez-Sanchez , A. , Orts-Escolano , S. , and Cazorla , M. Pedestrian Movement Direction Recognition Using Convolutional Neural Networks IEEE Transactions on Intelligent Transport Systems 2017