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学者姓名：张菁
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摘要 ：
A two-level hierarchical scheme for video-based person re-identification (re-id) is presented, with the aim of learning a pedestrian appearance model through more complete walking cycle extraction. Specifically, given a video with consecutive frames, the objective of the first level is to detect the key frame with lightweight Convolutional neural network (CNN) of PCANet to reflect the summary of the video content. At the second level, on the basis of the detected key frame, the pedestrian walking cycle is extracted from the long video sequence. Moreover, local features of Local maximal occurrence (LOMO) of the walking cycle are extracted to represent the pedestrian' s appearance information. In contrast to the existing walking-cycle-based person re-id approaches, the proposed scheme relaxes the limit on step number for a walking cycle, thus making it flexible and less affected by noisy frames. Experiments are conducted on two benchmark datasets: PRID 2011 and iLIDS-VID. The experimental results demonstrate that our proposed scheme outperforms the six state-of-art video-based re-id methods, and is more robust to the severe video noises and variations in pose, lighting, and camera viewpoint.
关键词 ：
Video‐ Video‐ identification identification based person re‐ based person re‐ Convolutional neural network Convolutional neural network Walking cycle extraction Walking cycle extraction Key frame detection Key frame detection
引用：
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| GB/T 7714 | Youjiao, Li , Li, Zhuo , Jiafeng, Li et al. A Hierarchical Scheme for Video-Based Person Re-identification Using Lightweight PCANet and Handcrafted LOMO Features [J]. | CHINESE JOURNAL OF ELECTRONICS , 2021 , 30 (2) : 289-295 . |
| MLA | Youjiao, Li et al. "A Hierarchical Scheme for Video-Based Person Re-identification Using Lightweight PCANet and Handcrafted LOMO Features" . | CHINESE JOURNAL OF ELECTRONICS 30 . 2 (2021) : 289-295 . |
| APA | Youjiao, Li , Li, Zhuo , Jiafeng, Li , Jing, Zhang . A Hierarchical Scheme for Video-Based Person Re-identification Using Lightweight PCANet and Handcrafted LOMO Features . | CHINESE JOURNAL OF ELECTRONICS , 2021 , 30 (2) , 289-295 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Since the era of we-media, live video industry has shown an explosive growth trend. For large-scale live video streaming, especially those containing crowd events that may cause great social impact, how to identify and supervise the crowd activity in live video streaming effectively is of great value to push the healthy development of live video industry. The existing crowd activity recognition mainly uses visual information, rarely fully exploiting and utilizing the correlation or external knowledge between crowd content. Therefore, a crowd activity recognition method in live video streaming is proposed by 3D-ResNet and regional graph convolution network (ReGCN). (1) After extracting deep spatiotemporal features from live video streaming with 3D-ResNet, the region proposals are generated by region proposal network. (2) A weakly supervised ReGCN is constructed by making region proposals as graph nodes and their correlations as edges. (3) Crowd activity in live video streaming is recognised by combining the output of ReGCN, the deep spatiotemporal features and the crowd motion intensity as external knowledge. Four experiments are conducted on the public collective activity extended dataset and a real-world dataset BJUT-CAD. The competitive results demonstrate that our method can effectively recognise crowd activity in live video streaming.
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| GB/T 7714 | Kang, Junpeng , Zhang, Jing , Li, Wensheng et al. Crowd activity recognition in live video streaming via 3D-ResNet and region graph convolution network [J]. | IET IMAGE PROCESSING , 2021 , 15 (14) : 3476-3486 . |
| MLA | Kang, Junpeng et al. "Crowd activity recognition in live video streaming via 3D-ResNet and region graph convolution network" . | IET IMAGE PROCESSING 15 . 14 (2021) : 3476-3486 . |
| APA | Kang, Junpeng , Zhang, Jing , Li, Wensheng , Zhuo, Li . Crowd activity recognition in live video streaming via 3D-ResNet and region graph convolution network . | IET IMAGE PROCESSING , 2021 , 15 (14) , 3476-3486 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Due to the complex background and spatial distribution, it brings great challenge to object detection in high-resolution remote sensing images. In view of the characteristics of various scales, arbitrary orientations, shape variations, and dense arrangement, a multiscale object detection method in high-resolution remote sensing images is proposed by using rotation invariance deep features driven by channel attention. First, a channel attention module is added to our feature fusion and scaling-based single shot detector (FS-SSD) to strengthen the long-term semantic dependence between objects for improving the discriminative ability of the deep features. Then, an oriented response convolution is followed to generate feature maps with orientation channels to produce rotation invariant deep features. Finally, multiscale objects are predicted in a high-resolution remote sensing image by fusing various scale feature maps with multiscale feature module in FS-SSD. Five experiments are conducted on NWPU VHR-10 dataset and achieve better detection performance compared with the state-of-the-art methods.
引用：
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| GB/T 7714 | Zhao, Xiaolei , Zhang, Jing , Tian, Jimiao et al. Multiscale object detection in high-resolution remote sensing images via rotation invariant deep features driven by channel attention [J]. | INTERNATIONAL JOURNAL OF REMOTE SENSING , 2021 , 42 (15) : 5754-5773 . |
| MLA | Zhao, Xiaolei et al. "Multiscale object detection in high-resolution remote sensing images via rotation invariant deep features driven by channel attention" . | INTERNATIONAL JOURNAL OF REMOTE SENSING 42 . 15 (2021) : 5754-5773 . |
| APA | Zhao, Xiaolei , Zhang, Jing , Tian, Jimiao , Zhuo, Li , Zhang, Jie . Multiscale object detection in high-resolution remote sensing images via rotation invariant deep features driven by channel attention . | INTERNATIONAL JOURNAL OF REMOTE SENSING , 2021 , 42 (15) , 5754-5773 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Live video hosted by streamer is being sought after by more and more Internet users. A few streamers show inappropriate action in normal live video content for profit and popularity, who bring great harm to the network environment. In order to effectively regulate the streamer behavior in live video, a strea-mer action recognition method in live video with spatial-temporal attention and deep dictionary learning is proposed in this paper. First, deep features with spatial context are extracted by a spatial attention net-work to focus on action region of streamer after sampling video frames from live video. Then, deep fea-tures of video are fused by assigning weights with a temporal attention network to learn the frame attention from an action. Finally, deep dictionary learning is used to sparsely represent the deep features to further recognize streamer actions. Four experiments are conducted on a real-world dataset, and the competitive results demonstrate that our method can improve the accuracy and speed of streamer action recognition in live video. (c) 2021 Elsevier B.V. All rights reserved.
关键词 ：
Streamer Streamer Action recognition Action recognition Live video Live video Spatial-temporal attention Spatial-temporal attention Deep dictionary learning Deep dictionary learning
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| GB/T 7714 | Li, Chenhao , Zhang, Jing , Yao, Jiacheng . Streamer action recognition in live video with spatial-temporal attention and deep dictionary learning [J]. | NEUROCOMPUTING , 2021 , 453 : 383-392 . |
| MLA | Li, Chenhao et al. "Streamer action recognition in live video with spatial-temporal attention and deep dictionary learning" . | NEUROCOMPUTING 453 (2021) : 383-392 . |
| APA | Li, Chenhao , Zhang, Jing , Yao, Jiacheng . Streamer action recognition in live video with spatial-temporal attention and deep dictionary learning . | NEUROCOMPUTING , 2021 , 453 , 383-392 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Although deep learning has reached a higher accuracy for video content analysis, it is not satisfied with practical application demands of porn streamer recognition in live video because of multiple parameters, complex structures of deep network model. In order to improve the recognition efficiency of porn streamer in live video, a deep network model compression method based on multimodal knowledge distillation is proposed. First, the teacher model is trained with visual-speech deep network to obtain the corresponding porn video prediction score. Second, a lightweight student model constructed with MobileNetV2 and Xception transfers the knowledge from the teacher model by using multimodal knowledge distillation strategy. Finally, porn streamer in live video is recognized by combining the lightweight student model of visualspeech network with the bullet screen text recognition network. Experimental results demonstrate that the proposed method can effectively drop the computation cost and improve the recognition speed under the proper accuracy.
关键词 ：
Lightweight student model Lightweight student model Knowledge distillation Knowledge distillation Live video Live video Multimodal Multimodal Porn streamer recognition Porn streamer recognition
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| GB/T 7714 | Wang Liyuan , Zhang Jing , Yao Jiacheng et al. Porn Streamer Recognition in Live Video Based on Multimodal Knowledge Distillation [J]. | CHINESE JOURNAL OF ELECTRONICS , 2021 , 30 (6) : 1096-1102 . |
| MLA | Wang Liyuan et al. "Porn Streamer Recognition in Live Video Based on Multimodal Knowledge Distillation" . | CHINESE JOURNAL OF ELECTRONICS 30 . 6 (2021) : 1096-1102 . |
| APA | Wang Liyuan , Zhang Jing , Yao Jiacheng , Zhuo Li . Porn Streamer Recognition in Live Video Based on Multimodal Knowledge Distillation . | CHINESE JOURNAL OF ELECTRONICS , 2021 , 30 (6) , 1096-1102 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Live video streaming platforms have attracted millions of streamers and daily active users. For profit and popularity accumulation, some streamers mix pornography content into live content to avoid online supervision. Therefore, accurate recognition of porn streamers in live video streaming has become a challenging task. Porn streamers in live video present multimodal characteristics including visual and acoustic content. Therefore, a porn streamer recognition method in live video streaming is proposed that uses attention-gated multimodal deep features. Our contribution includes the following: (1) multimodal deep features, i.e., spatial, motion and audio, are extracted from live video streaming using convolutional neural networks (CNNs), in which the temporal context of multimodal features is obtained with a bi-directional gated recurrent unit (Bi-GRU); (2) the tri-attention gated mechanism is applied to map the associations between different modalities by assigning higher weights to important features for further reduction in the redundancy of multimodal features; (3) porn streamers in live video streaming are recognized via the attention-gated multimodal deep features. Six experiments are conducted on a real-world dataset, and the competitive results demonstrate that our method can effectively recognize porn streamers in live video streaming.
关键词 ：
attention-gated attention-gated bi-directional gated recurrent unit bi-directional gated recurrent unit Computational modeling Computational modeling Feature extraction Feature extraction Live video streaming Live video streaming Logic gates Logic gates multimodal deep features multimodal deep features porn streamer recognition porn streamer recognition Redundancy Redundancy Streaming media Streaming media Task analysis Task analysis Visualization Visualization
引用：
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| GB/T 7714 | Wang, Liyuan , Zhang, Jing , Tian, Qi et al. Porn Streamer Recognition in Live Video Streaming via Attention-Gated Multimodal Deep Features [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2020 , 30 (12) : 4876-4886 . |
| MLA | Wang, Liyuan et al. "Porn Streamer Recognition in Live Video Streaming via Attention-Gated Multimodal Deep Features" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 30 . 12 (2020) : 4876-4886 . |
| APA | Wang, Liyuan , Zhang, Jing , Tian, Qi , Li, Chenhao , Zhuo, Li . Porn Streamer Recognition in Live Video Streaming via Attention-Gated Multimodal Deep Features . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2020 , 30 (12) , 4876-4886 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Existing deployed Unmanned Aerial Vehicles (UAVs) visual trackers are usually based on the correlation filter framework. Although thesemethods have certain advantages of lowcomputational complexity, the tracking performance of small targets and fast motion scenarios is not satisfactory. In this paper, we present a novel multilevel prediction Siamese network (MLPS) for object tracking in UAV videos, which consists of Siamese feature extraction module and multi-level prediction module. The multi-level prediction module can make full use of the characteristics of each layer features to achieve robust evaluation of targets with different scales. Meanwhile, for small-size target tracking, we design a residual feature fusion block, which is used to constrain the low-level feature representation by using high-level abstract semantics, and obtain the improvement of the tracker's ability to distinguish scene details. In addition, we propose a layer attention fusion block which is sensitive to the informative features of each layers to achieve adaptive fusion of different levels of correlation responses by dynamically balancing the multi-layer features. Sufficient experiments on several UAV tracking benchmarks demonstrate that MLPS achieves state-of-the-art performance and runs at a speed over 97 FPS. (c) 2020 Elsevier B.V. All rights reserved.
关键词 ：
Feature fusion Feature fusion Multi-level prediction Multi-level prediction Small target Small target UAV tracking UAV tracking
引用：
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| GB/T 7714 | Zhu, Mu , Zhang, Hui , Zhang, Jing et al. Multi-level prediction Siamese network for real-time UAV visual tracking [J]. | IMAGE AND VISION COMPUTING , 2020 , 103 . |
| MLA | Zhu, Mu et al. "Multi-level prediction Siamese network for real-time UAV visual tracking" . | IMAGE AND VISION COMPUTING 103 (2020) . |
| APA | Zhu, Mu , Zhang, Hui , Zhang, Jing , Zhuo, Li . Multi-level prediction Siamese network for real-time UAV visual tracking . | IMAGE AND VISION COMPUTING , 2020 , 103 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Live video hosted by streamers is being sought after by an increasing number of Internet users. Some streamers mix pornographic content with live video for profit and popularity, but this greatly harms the network environment. To effectively identify porn streamers, a multilevel fusion method of multimodal deep features for porn streamer recognition in live video is proposed in this paper. (1) Visual and audio features including spatial, audio, motion, and temporal context in live video are extracted by a multimodal deep network. (2) Audio-visual attention features are obtained by fusing visual and audio features at the feature level based on a multimodal attention mechanism. (3) Text features are extracted by using the bullet screen text network based on the BERT (bidirectional encoder representations from transformers) model after collecting text information from the viewers bullet screen comments. (4) The prediction results of the audio-visual deep network and the bullet screen text network are fused at the decision level to improve the porn streamer recognition accuracy. We build a real-world dataset of porn streamers and conduct experiments and demonstrate that our method can improve the porn streamer recognition accuracy. © 2020 Elsevier B.V.
关键词 ：
Behavioral research Behavioral research Character recognition Character recognition
引用：
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| GB/T 7714 | Wang, Liyuan , Zhang, Jing , Wang, Meng et al. Multilevel fusion of multimodal deep features for porn streamer recognition in live video [J]. | Pattern Recognition Letters , 2020 , 140 : 150-157 . |
| MLA | Wang, Liyuan et al. "Multilevel fusion of multimodal deep features for porn streamer recognition in live video" . | Pattern Recognition Letters 140 (2020) : 150-157 . |
| APA | Wang, Liyuan , Zhang, Jing , Wang, Meng , Tian, Jimiao , Zhuo, Li . Multilevel fusion of multimodal deep features for porn streamer recognition in live video . | Pattern Recognition Letters , 2020 , 140 , 150-157 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Unmanned aerial vehicles (UAVs) have been widely applied to various fields, facing mass imagery data, object detection in UAV imagery is under extensive research for its significant status in both theoretical study and practical applications. In order to achieve the accurate object detection in UAV imagery on the premise of real-time processing, a coarse-to-fine object detection method for UAV imagery using lightweight convolutional neural network (CNN) and deep motion saliency is proposed in this paper. The proposed method includes three steps: (1) Key frame extraction using image similarity measurement is performed on the UAV imagery to accelerate the successive object detection procedure; (2) Deep features are extracted by PeleeNet, a lightweight CNN, to achieve the coarse object detection on the key frames; (3) LiteFlowNet and objects prior knowledge is utilized to analyze the deep motion saliency map, which further helps to refine the detection results. The detection results on key frames propagate to the temporally nearest non-key frames to achieve the fine detection. Five experiments are conducted to verify the effectiveness of the proposed method on Stanford drone dataset (SDD). The experimental results demonstrate that the proposed method can achieve comparable detection speed but superior accuracy to six state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.
关键词 ：
Coarse-to-fine Coarse-to-fine Lightweight convolutional neural network (CNN) Lightweight convolutional neural network (CNN) Deep motion saliency Deep motion saliency Object detection Object detection Unmanned aerial vehicle (UAV) imagery Unmanned aerial vehicle (UAV) imagery
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| GB/T 7714 | Zhang, Jing , Liang, Xi , Wang, Meng et al. Coarse-to-fine object detection in unmanned aerial vehicle imagery using lightweight convolutional neural network and deep motion saliency [J]. | NEUROCOMPUTING , 2020 , 398 : 555-565 . |
| MLA | Zhang, Jing et al. "Coarse-to-fine object detection in unmanned aerial vehicle imagery using lightweight convolutional neural network and deep motion saliency" . | NEUROCOMPUTING 398 (2020) : 555-565 . |
| APA | Zhang, Jing , Liang, Xi , Wang, Meng , Yang, Liheng , Zhuo, Li . Coarse-to-fine object detection in unmanned aerial vehicle imagery using lightweight convolutional neural network and deep motion saliency . | NEUROCOMPUTING , 2020 , 398 , 555-565 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
DASH (Dynamic Adaptive Streaming over HTTP (HyperText Transfer Protocol)) as a universal unified multimedia streaming standard selects the appropriate video bitrate to improve the user's Quality of Experience (QoE) according to network conditions, client status, etc. Considering that the quantitative expression of the user's QoE is also a difficult point in itself, this paper researched the distortion caused due to video compression, network transmission and other aspects, and then proposes a video QoE metric for dynamic adaptive streaming services. Three-Dimensional Convolutional Neural Networks (3D CNN) and Long Short-Term Memory (LSTM) are used together to extract the deep spatial-temporal features to represent the content characteristics of the video. While accounting for the fluctuation in the quality of a video caused by bitrate switching on the QoE, other factors such as video content characteristics, video quality and video fluency, are combined to form the input feature vector. The ridge regression method is adopted to establish a QoE metric that enables to dynamically describe the relationship between the input feature vector and the value of the Mean Opinion Score (MOS). The experimental results on different datasets demonstrate that the prediction accuracy of the proposed method can achieve superior performance over the state-of-the-art methods, which proves the proposed QoE model can effectively guide the client's bitrate selection in dynamic adaptive streaming media services.
关键词 ：
deep spatial-temporal representation deep spatial-temporal representation DASH DASH quality of experience quality of experience metric metric mobile video mobile video
引用：
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| GB/T 7714 | Du, Lina , Zhuo, Li , Li, Jiafeng et al. Video Quality of Experience Metric for Dynamic Adaptive Streaming Services Using DASH Standard and Deep Spatial-Temporal Representation of Video [J]. | APPLIED SCIENCES-BASEL , 2020 , 10 (5) . |
| MLA | Du, Lina et al. "Video Quality of Experience Metric for Dynamic Adaptive Streaming Services Using DASH Standard and Deep Spatial-Temporal Representation of Video" . | APPLIED SCIENCES-BASEL 10 . 5 (2020) . |
| APA | Du, Lina , Zhuo, Li , Li, Jiafeng , Zhang, Jing , Li, Xioguang , Zhang, Hui . Video Quality of Experience Metric for Dynamic Adaptive Streaming Services Using DASH Standard and Deep Spatial-Temporal Representation of Video . | APPLIED SCIENCES-BASEL , 2020 , 10 (5) . |
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