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Zhong Jin; Zhen Lou; Jing-Yu Yang; Quan-sen Sun |
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Title |
Face detection using template matching and skin color information |
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2005 |
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International Conference on Intelligent Computing, 636–645 |
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Hefei (China) |
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Admin @ si @ JLY2005 |
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627 |
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Author |
Zhong Jin; Zhen Lou; Jing-Yu Yang; Quan-sen Sun |
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Title |
Face Detection using Template Matching and Skin-color Information |
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2007 |
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Neurocomputing, 70(4–6): 794–800 |
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Admin @ si @ JLY2007 |
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878 |
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Zhong Jin; Jing-Yu Yang; Zhen Lou |
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Title |
A luminance-conditional distribution model of skin color information |
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2005 |
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2005 Beijing International Conference on Imaging: Technology and Applications for the 21th Century, 280–281 |
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Beijing (China) |
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Admin @ si @ JYL2005 |
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628 |
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Zhong Jin; Franck Davoine; Zhen Lou; Jing-Yu Yang |
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Title |
A novel PCA-based Bayes classifier and face analysis |
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2006 |
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International Conference on Advances in Biometrics (ICB’06), LNCS 3832: 144–150 |
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Hong Kong |
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Admin @ si @ JDL2006 |
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624 |
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Author |
Zhong Jin; Franck Davoine; Zhen Lou |
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Title |
Facial expression analysis by using KPCA |
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2003 |
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IEEE International Conference on Robotics, Intelligent Systems and Signal Processing (IEEE RISSP 2003), pp736–741 |
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Changsha, Hunan, China |
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Admin @ si @ JDL2003 |
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431 |
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Zhong Jin; Franck Davoine; Zhen Lou |
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Title |
An Effective EM Algorithm for PCA Mixture Model |
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2004 |
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Structural and Statistical Pattern Recognition, Lecture Notes in Computer Science, 3138:626–634 |
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Lisbon, Portugal |
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Admin @ si @ JDL2004 |
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482 |
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Author |
Zhong Jin; Franck Davoine |
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Title |
Orthogonal ICA Representation Of Images |
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Miscellaneous |
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2004 |
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8th International Conference on Control, Automation, Robotics and Vision, 369–374 |
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Kunming (China) |
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Admin @ si @ JiD2004 |
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499 |
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Author |
Zhijie Fang; David Vazquez; Antonio Lopez |
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Title |
On-Board Detection of Pedestrian Intentions |
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2017 |
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Sensors |
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SENS |
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17 |
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10 |
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2193 |
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pedestrian intention; ADAS; self-driving |
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Abstract |
Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role.
During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors.
However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is
essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the
pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information. |
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ADAS; 600.085; 600.076; 601.223; 600.116; 600.118 |
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Admin @ si @ FVL2017 |
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2983 |
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Author |
Zhijie Fang; Antonio Lopez |
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Title |
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation |
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2018 |
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IEEE Intelligent Vehicles Symposium |
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1271 - 1276 |
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Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-ofthe-art results. |
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IV |
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ADAS; 600.124; 600.116; 600.118 |
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Admin @ si @ FaL2018 |
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3181 |
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Zhijie Fang; Antonio Lopez |
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Title |
Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation |
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2019 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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21 |
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11 |
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4773 - 4783 |
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Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs. |
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ADAS; 600.118 |
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Admin @ si @ FaL2019 |
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3305 |
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