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学者姓名:薛建儒

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Coarse-to-fine-grained method for image splicing region detection EI
期刊论文 | 2022 , 122 | Pattern Recognition
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Abstract :

In this study, we aim to improve the accuracy of image splicing detection. We propose a progressive image splicing detection method that can detect the position and shape of spliced region. Because image splicing is likely to destroy or change the consistent correlation pattern introduced by color filter array (CFA) interpolation process, we first used a covariance matrix to reconstruct the R, G and B channels of image and utilized the inconsistencies of the CFA interpolation pattern to extract forensics feature. Then, these forensics features were used to perform coarse-grained detection, and texture strength features were used to perform fine-grained detection. Finally, an edge smoothing method was applied to realize precise localization. As compared to the state-of-the-art CFA-based image splicing detection methods, the proposed method has a high-level detection accuracy and strong robustness against content-preserving manipulations and JPEG compression. © 2021

Keyword :

Covariance matrix Edge detection Image enhancement Digital forensics Image texture Feature extraction Interpolation Textures Image compression

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GB/T 7714 Wang, Xiaofeng , Wang, Yan , Lei, Jinjin et al. Coarse-to-fine-grained method for image splicing region detection [J]. | Pattern Recognition , 2022 , 122 .
MLA Wang, Xiaofeng et al. "Coarse-to-fine-grained method for image splicing region detection" . | Pattern Recognition 122 (2022) .
APA Wang, Xiaofeng , Wang, Yan , Lei, Jinjin , Li, Bin , Wang, Qin , Xue, Jianru . Coarse-to-fine-grained method for image splicing region detection . | Pattern Recognition , 2022 , 122 .
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Video Frame Prediction by Deep Multi-Branch Mask Network EI SCIE
期刊论文 | 2021 , 31 (4) , 1283-1295 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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Abstract :

Future frame prediction in video is one of the most important problem in computer vision, and useful for a range of practical applications, such as intention prediction or video anomaly detection. However, this task is challenging because of the complex and dynamic evolution of scene. The difficulty of video frame prediction is to model the inherent spatio-temporal correlation between frames and pose an adaptive and flexible framework for large motion change or appearance variation. In this paper, we construct a deep multi-branch mask network (DMMNet) which adaptively fuses the advantages of optical flow warping and RGB pixel synthesizing methods, i.e., the common two kinds of approaches in this task. In the procedure of DMMNet, we add mask layer in each branch to adaptively adjust the magnitude range of estimated optical flow and the weight of predicted frames by optical flow warping and RGB pixel synthesizing, respectively. In other words, we provide a more flexible masking network for motion and appearance fusion on video frame prediction. Exhaustive experiments on Caltech pedestrian and UCF101 datasets show that the proposed model can obtain favorable video frame prediction performance compared with the state-of-the-art methods. In addition, we also put our model into the video anomaly detection problem, and the superiority is verified by the experiments on UCSD dataset.

Keyword :

deep learning video anomaly detection Adaptive optics Optical imaging Synthesizers Optical distortion Optical computing Video frame prediction Optical network units multi-frame prediction multi-branch mask network Predictive models

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GB/T 7714 Li, Sen , Fang, Jianwu , Xu, Hongke et al. Video Frame Prediction by Deep Multi-Branch Mask Network [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2021 , 31 (4) : 1283-1295 .
MLA Li, Sen et al. "Video Frame Prediction by Deep Multi-Branch Mask Network" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 31 . 4 (2021) : 1283-1295 .
APA Li, Sen , Fang, Jianwu , Xu, Hongke , Xue, Jianru . Video Frame Prediction by Deep Multi-Branch Mask Network . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2021 , 31 (4) , 1283-1295 .
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Improve Regression Network on Depth Hand Pose Estimation with Auxiliary Variable EI SCIE
期刊论文 | 2021 , 31 (3) , 890-904 | IEEE Transactions on Circuits and Systems for Video Technology
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Abstract :

The regression based deep neural networks have achieved state-of-The-Arts performance on depth 3D hand pose estimation task. This paper focuses on improving the regression mapping between features and pose joints. Inspired by the distribution modeling ability of Variational Autoencoders, we introduce an auxiliary variable into the regression network. During training, the auxiliary variable is modeled by an inference distribution that learns the underlying structural kinematics of human hand. Different with other regression methods on hand poses, our network estimates the pose joints from input depth features and the learned auxiliary variable as well. We show that by introducing the auxiliary variable, the regression is benefited from 1) regularization modeled by inference distribution; and 2) prior information carried by the auxiliary model. The effectiveness of the proposed regression method is evaluated with extensively self-comparative experiments and in comparison with other regression methods on hand pose datasets. The proposed network is easy to train in an end-To-end manner and can work with various feature extraction methods. We apply the proposed regression method to an existing hand pose estimation system, and improves the estimation accuracy by 18.35% and 16.65% on public hand pose datasets. © 1991-2012 IEEE.

Keyword :

Deep neural networks Regression analysis

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GB/T 7714 Xu, Lu , Hu, Chen , Tao, Ji'An et al. Improve Regression Network on Depth Hand Pose Estimation with Auxiliary Variable [J]. | IEEE Transactions on Circuits and Systems for Video Technology , 2021 , 31 (3) : 890-904 .
MLA Xu, Lu et al. "Improve Regression Network on Depth Hand Pose Estimation with Auxiliary Variable" . | IEEE Transactions on Circuits and Systems for Video Technology 31 . 3 (2021) : 890-904 .
APA Xu, Lu , Hu, Chen , Tao, Ji'An , Xue, Jianru , Mei, Kuizhi . Improve Regression Network on Depth Hand Pose Estimation with Auxiliary Variable . | IEEE Transactions on Circuits and Systems for Video Technology , 2021 , 31 (3) , 890-904 .
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Perceptual hash-based coarse-to-fine grained image tampering forensics method EI SCIE
期刊论文 | 2021 , 78 | Journal of Visual Communication and Image Representation
WoS CC Cited Count: 1
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Abstract :

As an active forensic technology, perceptual image hash has important application in image content authenticity detection and integrity authentication. In this paper, we propose a hybrid-feature-based perceptual image hash method that can be used for image tampering detection and tampering localization. In the proposed method, we use the color features of image as global features, use point-based features and block-based features as local features, and combine with the structural features to generate intermediate hash code. Then we encrypt and randomize to generate the final hash code. Using this hash code, we present a coarse-to-fine grained forensics method for image tampering detection. The proposed method can realize object-level tampering localization. Abundant experimental results show that the proposed method is sensitive to content changes caused by malicious attacks, and the tampering localization precision achieves pixel level, and it is robust to a wide range of geometric distortions and content-preserving manipulations. Compared with the state-of-the-art schemes, the proposed scheme yields superior performance. © 2021 Elsevier Inc.

Keyword :

Authentication Digital forensics Hash functions

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GB/T 7714 Wang, Xiaofeng , Zhang, Qian , Jiang, Chuntao et al. Perceptual hash-based coarse-to-fine grained image tampering forensics method [J]. | Journal of Visual Communication and Image Representation , 2021 , 78 .
MLA Wang, Xiaofeng et al. "Perceptual hash-based coarse-to-fine grained image tampering forensics method" . | Journal of Visual Communication and Image Representation 78 (2021) .
APA Wang, Xiaofeng , Zhang, Qian , Jiang, Chuntao , Xue, Jianru . Perceptual hash-based coarse-to-fine grained image tampering forensics method . | Journal of Visual Communication and Image Representation , 2021 , 78 .
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Vehicle re-identification in tunnel scenes via synergistically cascade forests EI SCIE Scopus
期刊论文 | 2020 , 381 , 227-239 | NEUROCOMPUTING | IF: 5.719
WoS CC Cited Count: 2 SCOPUS Cited Count: 2
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Abstract :

Nowadays, numerous cameras have been equipped in tunnels for monitoring the tunnel safety, such as detecting fire, vehicle stopping, crashes, and so forth. Nevertheless, safety events in tunnels may occur in the blind zones not covered by the multi-camera monitoring systems. Therefore, this paper opens the challenging problem, tunnel vehicle re-identification (abbr. tunnel vehicle Re-ID), to make a between-camera speculation. Different from the open road scenes focused by existing vehicle Re-ID methods, tunnel vehicle Re-ID is more challenging because of poor light condition, low resolution, frequent occlusion, severe motion blur, high between-vehicle similarity, and so on. To be specific, we propose a synergistically cascade forests (SCF) model which aims to gradually construct the linking relation between vehicle samples with an increasing of alternative layers of random forest and extremely randomized forest. Through the modeling of SCF, we can restrict the influence of little inter-variation of different vehicle identities and large intra-variation of the same identities. This paper constructs a new and challenging tunnel vehicle dataset (Tunnel-VReID), consisting of 1000 pairs of tunnel vehicle images. Extensive experiments on our Tunnel-VReID demonstrate that the proposed method can outperform current state-of-the-art methods. Besides, in order to prove the adaptation ability of SCF, we also verify the superiority of SCF on a large-scale vehicle Re-ID dataset, named as VehiclelD, collected in open road scenes. (C) 2019 Elsevier B.V. All rights reserved.

Keyword :

Tunnel vehicle re-identification Curriculum learning Tunnel surveillance Extremely randomized forest Random forest

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GB/T 7714 Zhu, Rixing , Fang, Jianwu , Li, Shuying et al. Vehicle re-identification in tunnel scenes via synergistically cascade forests [J]. | NEUROCOMPUTING , 2020 , 381 : 227-239 .
MLA Zhu, Rixing et al. "Vehicle re-identification in tunnel scenes via synergistically cascade forests" . | NEUROCOMPUTING 381 (2020) : 227-239 .
APA Zhu, Rixing , Fang, Jianwu , Li, Shuying , Wang, Qi , Xu, Hongke , Xue, Jianru et al. Vehicle re-identification in tunnel scenes via synergistically cascade forests . | NEUROCOMPUTING , 2020 , 381 , 227-239 .
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An Efficient Sampling-Based Hybrid A? Algorithm for Intelligent Vehicles EI Scopus
会议论文 | 2020 , 2104-2109 | 31st IEEE Intelligent Vehicles Symposium, IV 2020
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Abstract :

In this paper, we propose an improved sampling-based hybrid A? (SBA?) algorithm for path planning of intelligent vehicles, which works efficiently in complex urban environments. Two main modifications are introduced into the traditional hybrid A? algorithm to improve its adaptivity in both structured and unstructured traffic scenes. Firstly, a hybrid potential field (HPF) model considering both traffic regulation and obstacle configuration is proposed to represent the vehicle's workspace, which is utilized as a heuristic function. Secondly, a set of directional motion primitives is generated by taking the prior topological structure of the workspace into account. The path planner using SBA? not only obeys traffic regulations in structured scenes but also is capable of exploring complex unstructured scenes rapidly. Finally, a post-optimization step is adopted to increase the feasibility of the path. The efficacy of the proposed algorithm is extensively validated and tested with an autonomous vehicle in real traffic scenes. The experimental results show that SBA? works well in complex urban environments. © 2020 IEEE.

Keyword :

Heuristic algorithms Intelligent vehicle highway systems Urban planning Vehicles

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GB/T 7714 Li, Gengxin , Xue, Jianru , Zhang, Lin et al. An Efficient Sampling-Based Hybrid A? Algorithm for Intelligent Vehicles [C] . 2020 : 2104-2109 .
MLA Li, Gengxin et al. "An Efficient Sampling-Based Hybrid A? Algorithm for Intelligent Vehicles" . (2020) : 2104-2109 .
APA Li, Gengxin , Xue, Jianru , Zhang, Lin , Wang, Di , Li, Yongqiang , Tao, Zhongxing et al. An Efficient Sampling-Based Hybrid A? Algorithm for Intelligent Vehicles . (2020) : 2104-2109 .
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Driving accident detection by self-supervised adversarial appearance-motion prediction in first-person videos EI Scopus
会议论文 | 2020 , 1083-1088 | 3rd International Conference on Unmanned Systems, ICUS 2020
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Abstract :

Driving accident is the event that occur unexpectedly and should be detected effectively for autonomous driving systems. In this paper, we propose a method based on adversarial appearance-motion prediction for driving accident detection in dashcam videos. The novelty of this method is to consider the predictability of the frame-level and object-level motion and appearance from the current to the future. Through a self-supervised adversarial learning between the real observation in next frame and the predicted motion and appearance, the objects that may occur accident are detected. In order to evaluate the accuracy of the detection results of our method, we evaluate the performance on A3D dataset, and the effectiveness of the method is validated. © 2020 IEEE.

Keyword :

Motion estimation Accidents

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GB/T 7714 Qiao, Jiahuan , Fang, Jianwu , Yan, Dingxin et al. Driving accident detection by self-supervised adversarial appearance-motion prediction in first-person videos [C] . 2020 : 1083-1088 .
MLA Qiao, Jiahuan et al. "Driving accident detection by self-supervised adversarial appearance-motion prediction in first-person videos" . (2020) : 1083-1088 .
APA Qiao, Jiahuan , Fang, Jianwu , Yan, Dingxin , Xue, Jianru . Driving accident detection by self-supervised adversarial appearance-motion prediction in first-person videos . (2020) : 1083-1088 .
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Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios EI Scopus
会议论文 | 2020 , 666-671 | 31st IEEE Intelligent Vehicles Symposium, IV 2020
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Abstract :

In this paper, we propose a lightweight system, RDS-SLAM, based on ORB-SLAM2, which can accurately estimate poses and build semantic maps at object level for dynamic scenarios in real time using only one commonly used Intel Core i7 CPU. In RDS-SLAM, three major improvements, as well as major architectural modifications, are proposed to overcome the limitations of ORB-SLAM2. Firstly, it adopts a lightweight object detection neural network in key frames. Secondly, an efficient tracking and prediction mechanism is embedded into the system to remove the feature points belonging to movable objects in all incoming frames. Thirdly, a semantic octree map is built by probabilistic fusion of detection and tracking results, which enables a robot to maintain a semantic description at object level for potential interactions in dynamic scenarios. We evaluate RDS-SLAM in TUM RGB-D dataset, and experimental results show that RDS-SLAM can run with 30.3 ms per frame in dynamic scenarios using only an Intel Core i7 CPU, and achieves comparable accuracy compared with the state-of-the-art SLAM systems which heavily rely on both Intel Core i7 CPUs and powerful GPUs. © 2020 IEEE.

Keyword :

Object detection Program processors Semantics Object tracking

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GB/T 7714 Chen, Xingyu , Xue, Jianru , Fang, Jianwu et al. Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios [C] . 2020 : 666-671 .
MLA Chen, Xingyu et al. "Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios" . (2020) : 666-671 .
APA Chen, Xingyu , Xue, Jianru , Fang, Jianwu , Pan, Yuxin , Zheng, Nanning . Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios . (2020) : 666-671 .
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Improving 3D Object Detection via Joint Attribute-oriented 3D Loss EI Scopus
会议论文 | 2020 , 951-956 | 31st IEEE Intelligent Vehicles Symposium, IV 2020
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3D object detection has become a hot topic in intelligent vehicle applications in recent years. Generally, deep learning has been the primary framework used in 3D object detection, and regression of the object location and classification of the objectness are the two indispensable components. In the process of training, the \ell_{n}\ (n=1,2) and the focal loss are considered as the frequent solutions to minimize the regression and classification loss, respectively. However, there are two problems to be solved in the existing methods. For regression component, there is a gap between evaluation metrics, e.g., 3D Intersection over Union (IoU), and the traditional regression loss. As for the classification component, confidence score exists ambiguous due to the binary label assignment of target. To solve these problems, we propose a loss by jointing 3D IoU and other geometric attributes (named as jointed attribute-oriented 3D loss), which can be directly used in optimizing the regression component. In addition, the jointed attribute-oriented 3D loss can assign a soft label for supervising the training of the classification. By incorporating the proposed loss function into several state-of-the-art 3D object detection methods, the significant performance improvement has been achieved on the KITTI benchmark. © 2020 IEEE.

Keyword :

Object recognition Object detection Deep learning Intelligent vehicle highway systems Benchmarking

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GB/T 7714 Ye, Zhen , Xue, Jianru , Dou, Jian et al. Improving 3D Object Detection via Joint Attribute-oriented 3D Loss [C] . 2020 : 951-956 .
MLA Ye, Zhen et al. "Improving 3D Object Detection via Joint Attribute-oriented 3D Loss" . (2020) : 951-956 .
APA Ye, Zhen , Xue, Jianru , Dou, Jian , Pan, Yuxin , Fang, Jianwu , Wang, Di et al. Improving 3D Object Detection via Joint Attribute-oriented 3D Loss . (2020) : 951-956 .
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Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition EI CPCI-S Scopus
会议论文 | 2020 , 1109-1118 | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data. Recently, there is a trend of using very deep feedforward neural networks to model the 3D coordinates of joints without considering the computational efficiency. In this paper, we propose a simple yet effective semantics-guided neural network (SGN) for skeleton-based action recognition. We explicitly introduce the high level semantics of joints (joint type and frame index) into the network to enhance the feature representation capability. In addition, we exploit the relationship of joints hierarchically through two modules, i.e., a joint-level module for modeling the correlations of joints in the same frame and a frame-level module for modeling the dependencies of frames by taking the joints in the same frame as a whole. A strong baseline is proposed to facilitate the study of this field. With an order of magnitude smaller model size than most previous works, SGN achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets.

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GB/T 7714 Zhang, Pengfei , Lan, Cuiling , Zeng, Wenjun et al. Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition [C] . 2020 : 1109-1118 .
MLA Zhang, Pengfei et al. "Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition" . (2020) : 1109-1118 .
APA Zhang, Pengfei , Lan, Cuiling , Zeng, Wenjun , Xing, Junliang , Xue, Jianru , Zheng, Nanning . Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition . (2020) : 1109-1118 .
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