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Global Context Encoding for Salient Objects Detection 24

Global Context Encoding for Salient Objects Detection

引用

24th International conference on pattern recognition (ICPR)

作者： Wang, Jingbo Xing, Yajie Zeng, Gang Peking Univ Key Lab Machine Percept Beijing Peoples R China

ISBN: (纸本)9781538637883

Deep convolutional neural networks (CNNs) have gained their reputation for the success in various tasks in computer vision, including salient objects detection. However, it remains a challenge that the CNNs have repeated downsample operators and always create low-resolution predictions, which tend to loss details and finer structure of images. To detect and segment the salient objects well, it is also necessary to merge high-level semantic information and low-level fine details simultaneously. thus, we propose a novel network structure with stage-wise refinement sub-structures. In addition, we exploit the essence of salient objects detection by encoding the global image context in a specifically designed module, which is applied to every stage of the refinement structure. So the coarse saliency map generated from the base CNN can be refined with low-level feature and global context information step-by-step. Experimental results have demonstrated that the proposed method outperforms the state-of-the-art approaches on four benchmark datasets.

关键词： Encoding Object detection Task analysis Semantics Feature extraction Image segmentation computer vision

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Motion-Depth: RGB-D Depth Map Enhancement with Motion and Depth in Complement 27

Motion-Depth: RGB-D Depth Map Enhancement with Motion and De...

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Hui, Tak-Wai Ngan, King Ngi Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

Low-cost RGB-D imaging system such as Kinect is widely utilized for dense 3D reconstruction. However, RGB-D system generally suffers from two main problems. the spatial resolution of the depth image is low. the depth image often contains numerous holes where no depth measurements are available. this can be due to bad infra-red reflectance properties of some objects in the scene. Since the spatial resolution of the color image is generally higher than that of the depth image, this paper introduces a new method to enhance the depth images captured by a moving RGB-D system using the depth cues from the induced optical flow. We not only fill the holes in the raw depth images, but also recover fine details of the imaged scene. We address the problem of depth image enhancement by minimizing an energy functional. In order to reduce the computational complexity, we have treated the textured and homogeneous regions in the color images differently. Experimental results on several RGB-D sequences are provided to show the effectiveness of the proposed method.

关键词： Image enhancement

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Single-Pedestrian Detection aided by Multi-pedestrian Detection

Single-Pedestrian Detection aided by Multi-pedestrian Detect...

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Ouyang, Wanli Wang, Xiaogang Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pat Rec Beijing Peoples R China

ISBN: (纸本)9780769549897

In this paper, we address the challenging problem of detecting pedestrians who appear in groups and have interaction. A new approach is proposed for single-pedestrian detection aided by multi-pedestrian detection. A mixture model of multi-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby multiple pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by single-and multi-pedestrian detectors, and to refine the single-pedestrian detection result with multi-pedestrian detection. It can integrate with any single-pedestrian detector without significantly increasing the computation load. 15 state-of-the-art single-pedestrian detection approaches are investigated on three widely used public datasets: Caltech, TUD-Brussels and Eth. Experimental results show that our framework significantly improves all these approaches. the average improvement is 9% on the Caltech-Test dataset, 11% on the TUD-Brussels dataset and 17% on the Eth dataset in terms of average miss rate. the lowest average miss rate is reduced from 48% to 43% on the Caltech-Test dataset, from 55% to 50% on the TUD-Brussels dataset and from 51% to 41% on the Eth dataset.

关键词： object detection pedestrians probability video surveillance

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M2DAR: Multi-View Multi-Scale Driver Action recognition with vision Transformer

M2DAR: Multi-View Multi-Scale Driver Action Recognition with...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Ma, Yunsheng Yuan, Liangqi Abdelraouf, Amr Han, Kyungtae Gupta, Rohit Li, Zihao Wang, Ziran Purdue University College of Engineering United States Toyota Motor North America InfoTech Labs United States

ISBN: (纸本)9798350302493

Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed videos, namely M2DAR, with a particular focus on detecting distracted driving behaviors. Our system features a weight-sharing, multi-scale Transformer-based action recognition network that learns robust hierarchical representations. Furthermore, we propose a new election algorithm consisting of aggregation, filtering, merging, and selection processes to refine the preliminary results from the action recognition module across multiple views. Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset demonstrate the effectiveness of our approach, where we achieved an overlap score of 0.5921 on the A2 test set. Our source code is available at https://***/PurdueDigitalTwin/M2DAR. © 2023 IEEE.

关键词： Statistical tests

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Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade 30

<i>Not All Pixels Are Equal</i>: Difficulty-Aware Semantic S...

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Xiaoxiao Liu, Ziwei Luo, Ping Loy, Chen Change Tang, Xiaoou Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pat Rec Beijing Peoples R China

ISBN: (纸本)9781538604571

We propose a novel deep layer cascade (LC) method to improve the accuracy and speed of semantic segmentation. Unlike the conventional model cascade (MC) that is composed of multiple independent models, LC treats a single deep model as a cascade of several sub-models. Earlier sub-models are trained to handle easy and confident regions, and they progressively feed-forward harder regions to the next sub-model for processing. Convolutions are only calculated on these regions to reduce computations. the proposed method possesses several advantages. First, LC classifies most of the easy regions in the shallow stage and makes deeper stage focuses on a few hard regions. Such an adaptive and 'difficulty-aware' learning improves segmentation performance. Second, LC accelerates both training and testing of deep network thanks to early decisions in the shallow stage. third, in comparison to MC, LC is an end-to-end trainable framework, allowing joint learning of all sub-models. We evaluate our method on PASCAL VOC and Cityscapes datasets, achieving state-of-the-art performance and fast speed.

关键词： Difficulty-Aware Semantic Segmentation Deep Layer Cascade cascades Deep layer submodel line controller segmentation Segmentation Semantics urban landscape

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Point pattern Feature Matching Method Based on Polar Coordinate 2

Point Pattern Feature Matching Method Based on Polar Coordin...

引用

2nd IEEE International conference on Software Engineering and Artificial Intelligence (SEAI) / 7th International Workshop on pattern recognition (IWPR)

作者： Xu, Qin Zhang, Lei City Univ Macau Inst Data Sci Macau Peoples R China Tongji Univ Dept Traff Informat & Control Engn Shanghai Peoples R China

ISBN: (数字)9781665482233

ISBN: (纸本)9781665482233

Infrared label point feature matching is one of the basic problems in the application direction of computer vision, pattern recognition and robot navigation and positioning. First, the center of the label point set and the weighted distance center are obtained in Cartesian coordinates, then the Cartesian coordinates are converted into polar coordinates to construct the position information feature vector of the label point set. Finally, using the method of spatial geometric constraints to compare each point in the label code to the feature vector, the matching result of the label code point set is obtained. the contributions of this paper are as follows: 1. A feature vector of label point set based on polar coordinate transformation is constructed, which makes the feature vector invariant to rotation, translation and illumination. 2. Using the combination of feature vector and spatial geometric constraints to screen label point pairs can effectively overcome the adverse effects of outlier points and noise. the final experiments demonstrate the effectiveness and robustness of the polar coordinate feature matching of label codes.

关键词： pattern recognition robot vision computer vision

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Constructing Robust Affinity Graphs for Spectral Clustering 27

Constructing Robust Affinity Graphs for Spectral Clustering

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zhu, Xiatian Loy, Chen Change Gong, Shaogang Queen Mary Univ London London E1 4NS England Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (纸本)9781479951178

Spectral clustering requires robust and meaningful affinity graphs as input in order to form clusters with desired structures that can well support human intuition. To construct such affinity graphs is non-trivial due to the ambiguity and uncertainty inherent in the raw data. In contrast to most existing clustering methods that typically employ all available features to construct affinity matrices with the Euclidean distance, which is often not an accurate representation of the underlying data structures, we propose a novel unsupervised approach to generating more robust affinity graphs via identifying and exploiting discriminative features for improving spectral clustering. Specifically, our model is capable of capturing and combining subtle similarity information distributed over discriminative feature subspaces for more accurately revealing the latent data distribution and thereby leading to improved data clustering, especially with heterogeneous data sources. We demonstrate the efficacy of the proposed approach on challenging image and video datasets.

关键词： Robust affinity graphs discriminative feature subspaces random forests spectral clustering subtle similarity weak proximity

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Multi-Modal three-Stream Network for Action recognition 24

Multi-Modal Three-Stream Network for Action Recognition

引用

24th International conference on pattern recognition (ICPR)

作者： Khalid, Muhammad Usman Yu, Jie Robert Bosch GmbH Comp Vis Res Lab Hildesheim Germany TU Dortmund Dept Comp Sci Dortmund Germany

ISBN: (纸本)9781538637883

Human action recognition in video is an active yet challenging research topic due to high variation and complexity of data. In this paper, a novel video based action recognition framework utilizing complementary cues is proposed to handle this complex problem. Inspired by the successful two stream networks for action classification, additional pose features are studied and fused to enhance understanding of human action in a more abstract and semantic way. Towards practices, not only ground truth poses but also noisy estimated poses are incorporated in the framework with our proposed pre-processing module. the whole framework and each cue are evaluated on varied benchmarking datasets as JHMDB, sub-JHMDB and Penn Action. Our results outperform state-of-the-art performance on these datasets and show the strength of complementary cues.

关键词： Semantics

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Beyond triplet loss: a deep quadruplet network for person re-identification 30

Beyond triplet loss: a deep quadruplet network for person re...

引用

30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Weihua Chen, Xiaotang Zhang, Jianguo Huang, Kaiqi CASIA CRIPAC&NLPR Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Dundee Comp Sch Sci & Engn Dundee Scotland CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

ISBN: (纸本)9781538604571

Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.

关键词： Probes Training Testing Machine learning Feature extraction Loss measurement

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Image-based Synthesis and Re-Synthesis of Viewpoints Guided by 3D Models 27

Image-based Synthesis and Re-Synthesis of Viewpoints Guided ...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Rematas, Konstantinos Ritschel, Tobias Fritz, Mario Tuytelaars, Tinne Katholieke Univ Leuven IMinds Louvain Belgium Max Planck Inst Informat Munich Germany Univ Saarland D-66123 Saarbrucken Germany

ISBN: (纸本)9781479951178

We propose a technique to use the structural information extracted from a set of 3D models of an object class to improve novel-view synthesis for images showing unknown instances of this class. these novel views can be used to "amplify" training image collections that typically contain only a low number of views or lack certain classes of views entirely (e. g. top views). We extract the correlation of position, normal, reflectance and appearance from computer-generated images of a few exemplars and use this information to infer new appearance for new instances. We show that our approach can improve performance of state-of-the-art detectors using real-world training data. Additional applications include guided versions of inpainting, 2D-to-3D conversion, super-resolution and non-local smoothing.

关键词： computer graphics computer vision image-based rendering object detection synthetic training data

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