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Travel mode choice prediction based on personalized recommendation model

IET INTELLIGENT TRANSPORT SYSTEMS 2023年第4期17卷 667-677页

作者： Lai, Zhilin Wang, Jing Zheng, Junjie Ding, Yuxing Wang, Cheng Zhang, Huizhen Huaqiao Univ Sch Comp Sci & Technol Xiamen Fujian Peoples R China Huaqiao Univ Xiamen Key Lab Comp Vis & Pattern Recognit Xiamen Fujian Peoples R China

The accurate prediction of individual travel mode in the city is necessary for the development of urban traffic intelligence. Through deep analysis on distribution of travel modes, policy makers can make proper policies to improve traffic conditions. With the development of sensing techniques and telecommunication technologies, it is easy to collect travel data of individuals and use some popular models to detect travel modes. However, existing methods are limited to aggregation analysis and these models suggest that travelers' preference mainly depend on the characteristics of travelers, little is known considering to extract individual travel preference from individual historical trips. Due to collect the user information is expensive and time-consuming, one of the difficult tasks facing the prediction of travel mode by traditional methods is the lack of user's profile. What is more, data of different travel modes are always imbalanced. Accordingly, the paper proposes a model Balance Multi Travel Mode Deep Learning Prediction (BMTM-DLP) for individual travel mode prediction inspired by the idea of recommender system. For the first time, the theory of recommender system is introduced into the field of individual travel mode choice prediction and we take advantage of the model to extract individual travel preference for travel mode prediction. Further, the focal loss function module is introduced into the model to reduce the impact of unbalanced categories. Experiments show that the model performs well on both datasets LPMC and XSCR, achieving 92.2% and 82.8% prediction accuracy in respective. It showed that the proposed model performed well in individual travel mode prediction.

关键词： traffic engineering computing BMTM-DLP LPMC travel data data handling individual travel preference XSCR Neural nets recommender system Information networks personalized recommendation model Traffic engineering computing Data handling techniques deep learning (artificial intelligence) road traffic recommender systems model balance multitravel mode deep learning prediction individual historical trips Search engines individual travel mode choice prediction

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Self-supervised non-rigid structure from motion with improved training of Wasserstein GANs

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IET compUTER visION 2023年第4期17卷 404-414页

作者： Wang, Yaming Peng, Xiangyang Huang, Wenqing Ye, Xiaoping Jiang, Mingfeng Lishui Univ Zhejiang Key Lab DDIMCCP Lishui Peoples R China Zhejiang Sci Tech Univ Pattern Recognit & Comp Vis Lab Hangzhou 310000 Peoples R China

This study proposes a self-supervised method to reconstruct 3D limbic structures from 2D landmarks extracted from a single view. The loss of self-consistency can be reduced by performing a random orthogonal projection of the reconstructed 3D structure. Thus, the training process can be self-supervised by using geometric self-consistency in the reconstruction-projection-reconstruction process. The self-supervised network mainly consists of graph convolution and Transformer encoders. This network is called the SS-Graphformer. By adding a discriminator, the SS-Graphformer is used as a generator to form a Wasserstein Generative Adversarial Network architecture with a Gradient Penalty to improve the accuracy of the reconstruction. It is experimentally demonstrated that the addition of the 2D structure discriminator can significantly improve the accuracy of the reconstruction.

关键词： computer vision neural nets

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Adversarial Attention Networks for Early Action recognition

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IEEE TRANSACTIONS ON EMERGING TOPICS IN compUTATIONAL INTELLIGENCE 2025年第2期9卷 1581-1594页

作者： Zhang, Hong-Bo Pan, Wei-Xiang Du, Ji-Xiang Lei, Qing Chen, Yan Liu, Jing-Hua Huaqiao Univ Dept Comp Sci & Technol Xiamen 361000 Peoples R China Huaqiao Univ Fujian Key Lab Big Data Intelligence & Secur Xiamen 361000 Peoples R China Huaqiao Univ Xiamen Key Lab Comp Vis & Pattern Recognit Xiamen 361000 Peoples R China Huaqiao Univ Fujian Prov Univ Key Lab Comp Vis & Machine Learning Xiamen 361000 Peoples R China

Early action recognition endeavors to deduce the ongoing action by observing partial video, presenting a formidable challenge due to limited information available in the initial stages. To tackle this challenge, we introduce an innovative adversarial attention network based on generative adversarial networks. This network leverages the characteristics of both the generator and discriminator to generate unobserved action information from partial video input. The proposed method comprises a cross attention generator, self Attention discriminator, and feature fusion module. The cross attention generator captures temporal relationships in input action sequences, generating discriminative unobserved action information. The self attention discriminator adds global attention to the input sequence, capturing global context information for accurate evaluation of consistency in generated unobserved feature from cross attention generator. Finally, the feature fusion module helps the model obtain richer and more comprehensive feature representations. The proposed method is evaluated through experiments on the HMDB51, UCF101 and Something-Something v2 datasets. Experimental results demonstrate that the proposed approach outperforms existing methods across different observation ratios. Detailed ablation studies confirm the effectiveness of each component in the proposed method.

关键词： Early action recognition adversarial attention network cross attention generator self attention discriminator feature fusion module Early action recognition adversarial attention network cross attention generator self attention discriminator feature fusion module

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Interaction Confidence Attention for Human-Object Interaction Detection

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INTERNATIONAL JOURNAL OF compUTER visION 2025年 1-20页

作者： Zhang, Hong-Bo Lin, Wang-Kai Su, Hang Lei, Qing Liu, Jing-Hua Du, Ji-Xiang Huaqiao Univ Dept Comp Sci & Technol Xiamen 361021 Peoples R China Huaqiao Univ Fujian Key Lab Big Data Intelligence & Secur Xiamen 361021 Peoples R China Huaqiao Univ Xiamen Key Lab Comp Vis & Pattern Recognit Xiamen 361021 Peoples R China Fujian Prov Univ Huaqiao Univ Key Lab Comp Vis & Machine Learning Xiamen 361021 Peoples R China

In human-object interaction (HOI) detection task, ensuring that interactive pairs receive higher attention weights while reducing the weight of non-interaction pairs is imperative for enhancing HOI detection accuracy. Guiding attention learning is also a key aspect of existing transformer-based algorithms. To tackle this challenge, this study proposes a novel approach termed Interaction Confidence Score Learning Attention (ICSLA), which introduces weakening and augmentation operations into the original attention weight calculation and feature extraction processes. In ICSLA, feature learning is coupled with confidence score learning, simultaneously. Leveraging ICSLA, a new and universal decoder is devised, establishing a transformer-based one-stage HOI detection architecture. Experimental results demonstrate the effectiveness of the proposed method in improving HOI detection accuracy, offering valuable insights for further optimization of attention mechanisms.

关键词： Human-object interaction detection Interaction confidence score learning attention Weakening operation Augmentation operation

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Lightweight Network For Video Motion Magnification 23

Lightweight Network For Video Motion Magnification

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23rd IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Singh, Jasdeep Murala, Subrahmanyam Kosuru, G. Sankara Raju Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Ropar India

ISBN: (纸本)9781665493468

Video motion magnification provides information to understand the subtle changes present in objects for applications like industrial, healthcare, sports, etc. Most state-of-the-art (SOTA) methods use hand-crafted bandpass filters, which require prior information for the motion magnification, produces ringing artifacts, and small magnification etc. While others use deep-learning based techniques for higher magnification, but their output suffers from artificially induced motion, distortions, blurriness, etc. Further, SOTA methods are computationally complex, which makes them less suitable for real-time applications. To address these problems, we proposed deep learning based simple yet effective solution for motion magnification. The proposed method uses a feature sharing and appearance encoder for better motion magnification with fewer distortions, artifacts etc. Additionally, for reducing magnification of noise and other unwanted changes, proxy-model based training is proposed. A computationally lightweight model (similar to 0.12 M parameters) is proposed along with the base model. The performance of the proposed models is tested qualitatively and quantitatively, with the SOTA methods. Results demonstrate the effectiveness of the proposed lightweight and base model over the existing SOTA methods.

关键词： Deep learning Training computational modeling Lighting Medical services Distortion Manipulators

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Part-based tracking for object pose estimation

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2023年第5期20卷 99页

作者： Ye, Shuang Ye, Jianhong Lei, Qing Huaqiao Univ Sch Comp Sci & Technol Xiamen Peoples R China Huaqiao Univ Xiamen Key Lab Comp Vis & Pattern Recognit Xiamen 361000 Peoples R China Huaqiao Univ Fujian Prov Univ Key Lab Comp Vis & Machine Learning Xiamen Peoples R China

Object pose estimation is crucial in human-computer interaction systems. The traditional point-based detection approaches rely on the robustness of feature points, the tracking methods utilize the similarity between frames to improve the speed, while the recent studies based on neural networks concentrate on solving specific invariance problems. Different from these methods, PTPE (Part-based Tracking for Pose Estimation) proposed in this paper focuses on how to balance the speed and accuracy under different conditions. In this method, the point matching is transformed into the part matching inside an object to enhance the reliability of the features. Additionally, a fast interframe tracking method is combined with learning models and structural information to enhance robustness. During tracking, multiple strategies are adopted for the different parts according to the matching effects evaluated by the learning models, so as to develop the locality and avoid the time consumption caused by undifferentiated full frame detection or learning. In addition, the constraints between parts are applied for parts detection optimization. Experiments show that PTPE is efficient both in accuracy and speed, especially in complex environments, when compared with classical algorithms that focus only on detection, interframe tracking, self-supervised models, and graph matching.

关键词： Object pose estimation Part-based tracking Local features matching Detection optimization Object tracking Frame-by-frame tracking

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Multi-Branch Enhanced Discriminative Network for Vehicle Re-Identification

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第2期25卷 1263-1274页

作者： Lian, Jiawei Wang, Da-Han Wu, Yun Zhu, Shunzhi Xiamen Univ Technol Sch Comp & Informat Engn Fujian Key Lab Pattern Recognit & Image Understand Xiamen 361024 Peoples R China

Vehicle re-identification (ReID) is the task of identifying the same vehicle across numerous cameras. This is a complex classification task, and the fine-grained information and strong discrimination features have proven to be effective in handling the re-identification classification task. However, most existing methods focuses on extracting local area features in combination with global features, while exploring subtle distinguishing features, which is a difficult task, remains an open problem and unsolved. In this paper, we propose a multi-branch enhanced discriminative network (MED) to better extract subtle distinguishing features that have high discriminative power to improve the ReID performance. In the proposed MED method, each feature map obtained by convolutional neural network (CNN) is divided into 4 spatial sub-maps, on each of which, the vertical and the horizontal branches are used to extract the subtle distinguishing features intrinsically contained in sub-areas. The vertical and the horizontal branches are combined with the global branch to perform the ReID task. Moreover, our proposed method is capable of extracting rich fine-grained features without the need of extra manual annotation while maintaining a simple design structure. We conducted extensive experiments on the vehicle ReID datasets (VehicleID and VeRi-776), showing that the proposed MED method outperforms most existing methods. Further, we directly apply the MED method to the pedestrian ReID problem on the Market-1501, DUKEMTMC, and MSMT17 datasets, achieving the state-of-the-art (SOTA) performance as well. This demonstrates that the proposed method has good generality and can be flexibly applied to the ReID tasks.

关键词： Feature extraction Task analysis Data mining Pedestrians Annotations Manuals Vehicle dynamics Vehicle re-identification multi-branch deep learning intelligent transportation systems

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Character-Aware Sampling and Rectification for Scene Text recognition

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 649-661页

作者： Li, Ming Fu, Bin Zhang, Zhengfu Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen 518055 Peoples R China Shanghai AI Lab Shanghai 200030 Peoples R China

Curved scene text recognition is a challenging task in multimedia society due to large shape and texture variance. Previous methods address this challenge by extracting and rectifying text line with equidistantly sampling, which ignore character level information and lead to distorted characters. To address this issue, this paper proposes a Character-Aware Sampling and Rectification (CASR) module, which rectifies irregular text instance according to the location and orientation information of each individual character. Specifically, CASR regards each character as a basic unit and predicts the character-level attributes for sampling and rectification. Our module not only exploits detailed character information to obtain better rectification of text line, but also employs character-level supervision in training process. In addition, CASR provides a plug-and-play module which can be easily incorporated to existing text recognition pipeline. Extensive experiments on several benchmarks demonstrate that our method obtains more accurate rectified text instances and achieves promising performance. We will release our code and models in the future.

关键词： Scene text recognition scene optical character recognition deep learning

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SMG: A Micro-gesture Dataset Towards Spontaneous Body Gestures for Emotional Stress State Analysis

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INTERNATIONAL JOURNAL OF compUTER visION 2023年第6期131卷 1346-1366页

作者： Chen, Haoyu Shi, Henglin Liu, Xin Li, Xiaobai Zhao, Guoying Univ Oulu Ctr Machine Vis & Signal Anal CMVS Oulu Finland Lappeenranta Lahti Univ Technol LUT Sch Engn Sci Comp Vis & Pattern Recognit Lab Lappeenranta Finland

We explore using body gestures for hidden emotional state analysis. As an important non-verbal communicative fashion, human body gestures are capable of conveying emotional information during social communication. In previous works, efforts have been made mainly on facial expressions, speech, or expressive body gestures to interpret classical expressive emotions. Differently, we focus on a specific group of body gestures, called micro-gestures (MGs), used in the psychology research field to interpret inner human feelings. MGs are subtle and spontaneous body movements that are proven, together with micro-expressions, to be more reliable than normal facial expressions for conveying hidden emotional information. In this work, a comprehensive study of MGs is presented from the computer vision aspect, including a novel spontaneous micro-gesture (SMG) dataset with two emotional stress states and a comprehensive statistical analysis indicating the correlations between MGs and emotional states. Novel frameworks are further presented together with various state-of-the-art methods as benchmarks for automatic classification, online recognition of MGs, and emotional stress state recognition. The dataset and methods presented could inspire a new way of utilizing body gestures for human emotion understanding and bring a new direction to the emotion AI community. The source code and dataset are made available: .

关键词： Micro-gestures Gesture recognition Emotion recognition Statistical modeling Deep learning Affective computing

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FASNet: Feature Aggregation and Sharing Network for Image Inpainting

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IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 1664-1668页

作者： Phutke, Shruti S. Murala, Subrahmanyam Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Rupnagar 140001 Punjab India

Image inpainting is a reconstruction method, where a corrupted image consisting of holes is filled with the most relevant contents from the valid region of an image. To inpaint an image, we have proposed a lightweight cascaded architecture with 2.5 M parameters consisting of encoder feature aggregation block (FAB) with decoder feature sharing (DFS) inpainting network followed by a refinement network. Initially, the FAB with DFS (inpainting) generator network is proposed which comprises of multi-level feature aggregation mechanism and feature sharing decoder. The FAB makes use of multi-scale spatial channel-wise attention to fuse weighted features from all the encoder levels. The DFS reconstructs the inpainted image with multi-scale and multi-receptive feature sharing in order to inpaint the image with smaller to larger hole regions effectively. Further, the refinement generator network is proposed for refining the inpainted image from the inpainting generator network. The effectiveness of proposed architecture is verified on CelebA-HQ, Paris Street View (PARIS_SV) and Places2 datasets corrupted using publicly available NVIDIA mask dataset. Extensive result analysis with detailed ablation study prove the robustness of the proposed architecture over state-of-the-art methods for image inpainting.

关键词： Feature extraction Decoding Training Image edge detection Convolution Image reconstruction Task analysis Adversarial learning edge refinement feature aggregation feature sharing image inpainting

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