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Article
Computer Science, Information Systems
Mingjie Wang et al.
Summary: This paper proposes a novel network called STNet for accurate crowd counting. STNet consists of two key components: Scale-Tree Diversity Enhancer and Multi-level Auxiliator. By enriching scale diversity and exploiting shared characteristics at multiple levels, STNet can significantly improve the accuracy of crowd counting.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Multidisciplinary Sciences
Lijia Deng et al.
Summary: How to allocate existing medical resources reasonably, alleviate hospital congestion, and improve the patient experience are common challenges faced by all hospitals. The combination of artificial intelligence and the medical field is mostly focused on disease diagnosis and lacks successful applications in medical management. In this study, a combination of image recognition and convolutional neural networks is used to analyze real-time waitlist numbers and measure congestion based on psychology and architecture. Factors contributing to congestion are analyzed by calculating diagnosis time and time-consuming after diagnosis. The research findings indicate congestion and waste of medical resources in the emergency department, highlighting the potential of using artificial intelligence to effectively investigate hospital congestion.
SCIENTIFIC REPORTS
(2023)
Article
Engineering, Electrical & Electronic
Pierre Maillard et al.
Summary: This article presents a platform and design approach for enabling radiation-tolerant deep learning acceleration. The platform includes solutions to mitigate radiation-induced interrupts and network datapath corruptions. Test results show significant improvements in system response and reduction in errors using the radiation-tolerant platform compared to standard nonmitigated approaches.
IEEE TRANSACTIONS ON NUCLEAR SCIENCE
(2023)
Article
Computer Science, Information Systems
Dingkang Liang et al.
Summary: This paper focuses on the crowd localization task and proposes a novel FIDT map for accurate instance localization. Experimental results demonstrate that the proposed method performs well in various scenarios and exhibits superior robustness in negative and extremely dense scenes.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Engineering, Civil
Honghui Xu et al.
Summary: In this paper, a more efficient vehicle counting method is proposed for city traffic management by utilizing a cooperative learning framework between multiple city cameras and an edge server. By implementing a lightweight feature extraction scheme and novel vehicle counting models, more accurate and time-efficient vehicle counting is achieved.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Qian Wang et al.
Summary: Automatic crowd behaviour analysis is a crucial task for intelligent transportation systems, and crowd counting plays a key role in this analysis. Recent advancements in deep convolutional neural networks have led to significant progress in crowd counting. This paper evaluates the performance of the baseline Inception-v3 model on commonly used crowd counting datasets, achieving surprisingly good results comparable to or better than existing models. Furthermore, a novel Segmentation Guided Attention Network (SGANet) with Inception-v3 as the backbone and a curriculum loss is proposed, which outperforms prior arts, attaining state-of-the-art performance on multiple datasets.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Mahesh Kumar Krishna Reddy et al.
Summary: This paper addresses the problem of image-based crowd counting and proposes a new problem setup called unlabeled scene-adaptive crowd counting. By using unlabeled images from the target scene, the proposed problem setup aims to adapt the crowd counting model to specific scenes. The authors introduce the AdaCrowd framework, which consists of a crowd counting network and a guiding network, and demonstrate its effectiveness through experimental results.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Computer Science, Information Systems
Yu-Jen Ma et al.
Summary: This paper proposes a novel SpatioTemporal convolutional Dense Network (STDNet) for video-based crowd counting problem. The network decomposes 3D convolution and utilizes 3D spatiotemporal dilated dense convolution to alleviate the rapid growth of model size. The combination of dilated convolution and channel attention block enhances feature representations. A new patch-wise regression loss (PRL) is proposed to improve the original pixel-wise loss for better convergence. Experimental results demonstrate the superiority of STDNet over state-of-the-art methods in video-based crowd counting.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Computer Science, Artificial Intelligence
Ruihan Hu et al.
Summary: This paper proposes a hybrid classical-quantum network based on quantum feature extraction and distance compensation to solve crowd counting and localization problems. By using quantum feature extraction module, multi-scale feature extraction module, and refine distance compensating module to fuse feature extraction branches, this method achieves better performance in handling crowd scenes and localization tasks compared to existing methods.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jia Wan et al.
Summary: Crowd counting is an important topic in computer vision, which uses density maps generated from ground-truth dot maps and deep learning models to improve counting performance. However, the hand-crafted methods used for generating density maps may not be optimal for specific networks or datasets. To address this, an adaptive density map generator is proposed, which is trained jointly with a counter within an end-to-end framework.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Fusen Wang et al.
Summary: This paper proposes a hybrid attention network (HAN) that addresses background noise and head scale variation by incorporating progressive embedding scale context information. By building parallel spatial attention and channel attention modules and embedding scale-context in the network, the proposed method focuses on the human head area and reduces counting errors caused by perspective and head scale variation. A progressive learning strategy is introduced by cascading multiple hybrid attention modules to gradually integrate different scale-context information.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yuehai Chen et al.
Summary: In this paper, a novel feedback network with Region-Aware block (RANet) is proposed to tackle the common problems of background noise and scale variation in crowd counting. The RANet incorporates human's Top-Down visual perception mechanism by generating priority maps and adaptively encoding contextual information. Experimental results demonstrate that our method outperforms state-of-the-art approaches on multiple public datasets.
Article
Computer Science, Information Systems
Dingkang Liang et al.
Summary: This paper proposes TransCrowd, a weakly-supervised crowd counting method based on transformers. By utilizing the self-attention mechanism of transformers, TransCrowd effectively extracts semantic crowd information, addressing the limited receptive fields for context modeling in traditional CNN methods. Experiments show that TransCrowd outperforms other weakly-supervised CNN methods and achieves competitive performance compared to some fully-supervised counting methods.
SCIENCE CHINA-INFORMATION SCIENCES
(2022)
Article
Environmental Sciences
Zijing Liu et al.
Summary: This study established a multifunctional decision-making framework for evaluating the suitability of green infrastructure (GI) based on regional pollution load intensity, slope, available space, and constructible area. The framework considers the environmental and ecological functions of GI and can be extended to other cities to assess GI preference.
Article
Computer Science, Artificial Intelligence
Qi Wang et al.
Summary: This paper proposes a neuron linear transformation (NLT) method to address domain shift in cross-domain crowd counting, achieving top performance compared with other domain adaptation methods on six real-world datasets. The NLT method exploits domain factor and bias weights to learn the domain shift, demonstrating resilience and effectiveness through ablation studies.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Qi Wang et al.
Summary: This article proposes a lightweight model and a density-aware curriculum learning training strategy for crowd counting. The experimental results demonstrate outstanding performance and effectiveness of these methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Jiwei Chen et al.
Summary: This paper proposes a hard example focusing (HEF) algorithm to address the problem of difficult examples in crowd counting, and effectively handles scale variation by using a multiscale semantic refining strategy. Experimental results demonstrate the superiority of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Lijia Deng et al.
Summary: Cell segmentation and counting are important and time-consuming steps in biomedical research. Traditional counting methods require exact cell locations, but there are few datasets with detailed object coordinates. To overcome this, we propose a GAN-based multi-task model and a novel loss function. Our method achieves excellent results in cell counting and segmentation, and significantly improves image processing speed.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Vishwanath A. Sindagi et al.
Summary: Introduced a new large-scale unconstrained crowd counting dataset and proposed a novel crowd counting network. The dataset contains diverse scenarios and environmental conditions, as well as rich annotations. The proposed network gradually generates crowd density maps through residual learning, guided by a confidence weighting mechanism, and achieves significant improvements.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Haipeng Xiong et al.
Summary: This paper demonstrates that formulating counting as a classification task performs better than regression due to imprecise ground truth local counts. The authors propose a novel discrete-constrained regression method, which shows higher accuracy in crowd counting and age estimation tasks compared to classification and standard regression.
COMPUTER VISION, ECCV 2022, PT XXIV
(2022)
Proceedings Paper
Acoustics
Mohsen Zand et al.
Summary: In this study, a multitask approach for crowd counting and person localization in a unified framework is proposed. By learning multiscale representations of encoded crowd images and subsequently fusing them, the model benefits from a multitask solution. The model achieves strong results on both counting and localization tasks, with high accuracy in crowd location identification.
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
(2022)
Proceedings Paper
Acoustics
Yi Hou et al.
Summary: In this article, a simulated crowd counting dataset CrowdX is proposed, which enhances the performance of existing algorithms in crowd counting. The analysis of the dataset reveals the impact of factors such as background, camera angle, human density, and resolution on crowd counting.
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhi-Qi Cheng et al.
Summary: Previous research suggests that improving the spatial invariance of convolutional networks is crucial for object counting. However, strict pixel-level spatial invariance can lead to overfit noise in density map generation. In this study, we propose the use of locally connected Gaussian kernels instead of the original convolution filter to estimate the spatial position in the density map, in order to overcome annotation noise. We also introduce a low-rank approximation accompanied with translation invariance to approximate massive Gaussian convolution. Experimental results demonstrate that our methods outperform state-of-the-art approaches in object counting.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Hui Lin et al.
Summary: This paper presents a Multifaceted Attention Network (MAN) to deal with the challenging task of crowd counting. The proposed network incorporates global attention, learnable local attention, and instance attention to enhance the performance of Transformer models in local spatial relation encoding.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Weibo Shu et al.
Summary: This paper investigates crowd counting in the frequency domain, proposing a novel method that transforms the density map into the frequency domain and utilizes the properties of the characteristic function. The experimental results demonstrate the competitiveness of the proposed method on multiple benchmark datasets.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Shenjian Gong et al.
Summary: Recently, there has been increasing attention on crowd density estimation. The main challenge lies in obtaining high-quality manual annotations on a large amount of training data. To overcome this reliance on annotations, previous works have applied unsupervised domain adaptation (UDA) techniques to transfer knowledge from synthetic data to real-world datasets. However, current state-of-the-art methods either rely on external data for auxiliary task training or use expensive coarse-to-fine estimation. In this work, we propose a novel adversarial learning based method that is simple and efficient. To reduce the domain gap between synthetic and real data, we introduce a bi-level alignment framework consisting of task-driven data alignment and fine-grained feature alignment. Unlike previous domain augmentation methods, we incorporate AutoML to search for an optimal transform on the source data for improved downstream task performance. Additionally, we perform fine-grained alignment for foreground and background separately to alleviate alignment difficulties. Our approach outperforms existing methods by a large margin on five real-world crowd counting benchmarks. It is also easy to implement and efficient. The code is publicly available at https://github.com/Yankeegsj/BLA.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2022)
Article
Computer Science, Artificial Intelligence
Li Dong et al.
Summary: In this article, a new network model called CLRNet is proposed for generating high-quality crowd density maps in videos for crowd counting. By introducing a cross locality relation module and a scene consistency attention map, this model can better model the local dependencies between pixels and enhance the features, resulting in improved accuracy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hong Mo et al.
Summary: In this paper, the authors propose an attention-guided collaborative counting module (AGCCM) to address the scale diversity problem in crowd counting. The AGCCM promotes collaboration among branches by recombining their outputs. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Haopeng Li et al.
Summary: The paper proposes a new method for video crowd localization, which models the spatial-temporal dependencies of human mobility using multi-focus Gaussian neighborhood attention. They develop a unified neural network to accurately locate head centers in video clips. They also introduce a large-scale crowd video benchmark for future research in this field.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Xin Zhong et al.
Summary: In this paper, an improved Normed-Deformable Convolution (NDConv) method is proposed to address the issue of crowd counting. By learning the coordinate offsets of the sampling points, the heads are covered more uniformly, leading to more complete head features and improved counting performance.
IEEE SIGNAL PROCESSING LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Qi Wang et al.
Summary: This paper explores crowd analysis using computer vision techniques, focusing on pixel-wise crowd understanding. By developing a synthetic dataset called GCC Dataset and proposing two methods to improve crowd understanding, the study aims to achieve better performance in real-world scenarios.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2021)
Article
Computer Science, Software Engineering
Sultan Daud Khan et al.
Summary: In high density crowds, crowd counting is important for safety and management. Existing methods use regression models to count people, but they cannot localize individuals. Detection-based crowd counting faces challenges due to scale, pose, and appearance variations. A proposed framework addresses scale variations using specialized convolutional neural networks, showing significant improvement over existing methods on benchmark datasets like UCF-QNRF and UCSD.
Article
Computer Science, Artificial Intelligence
Hang Yu et al.
Summary: This paper surveys the applications of convolutional neural networks in medical image analysis, reviews commonly used CNN models and tasks in various medical diagnosis areas. The challenges and future research directions of CNN in medical image analysis are discussed.
Article
Computer Science, Artificial Intelligence
Yinjie Lei et al.
Summary: This paper focuses on weakly-supervised crowd counting, where a model is learned from a small amount of location-level annotations and a large amount of count-level annotations. The study reveals that directly regressing the integral of density maps to object count is not satisfactory, and proposes a method to enforce consistency between density maps and object count for better performance. Through experiments, the effectiveness of the proposed weakly-supervised method is validated and shown to outperform existing solutions.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Sultan Daud Khan et al.
Summary: The proposed deep network effectively tackles the challenge of people counting in high-density environments by leveraging hierarchical features from different convolutional layers and utilizing image pyramid technology. Through experiments on benchmark datasets, the framework outperforms other state-of-the-art methods by achieving low Mean Absolute Error (MAE) and Mean Square Error (MSE) values.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
(2021)
Article
Automation & Control Systems
Yuan Zhou et al.
Summary: A multiscale generative adversarial network (MS-GAN) is proposed in this article to generate high-quality crowd density maps and accurately estimate crowd counts in complex scenes. By combining multiscale generator and adversarial network, the quality of density maps is improved, leading to better performance compared to current state-of-the-art methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Automation & Control Systems
Junyu Gao et al.
Summary: With the advancement of deep neural networks, the performance of crowd counting and pixel-wise density estimation has been improving, but challenges persist. A synthetic crowd dataset released recently helps address the difficulty in collecting data, but the domain gap between real data and synthetic images hinders model performance. To tackle this, a domain-adaptation-style crowd counting method is proposed in this article, which effectively adapts the model from synthetic data to specific real-world scenes.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Yanyu Chen et al.
Summary: This paper evaluates and warns security risks of large-scale group activities based on the random forest algorithm, achieving a maximum classification accuracy rate of 0.86 through model optimization and training experiments, demonstrating the algorithm's strong predictive ability in risk assessment.
PATTERN RECOGNITION LETTERS
(2021)
Article
Engineering, Civil
Xinghao Ding et al.
Summary: Crowd counting is crucial in intelligent transportation systems, but challenges like occlusion, perspective distortion, and complex backgrounds make it difficult to achieve accuracy. This study introduces a novel CNN model and a new evaluation method for measuring density map accuracy, outperforming existing methods. Evaluation on cross-scene datasets shows promising performance of the proposed method.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Qi Wang et al.
Summary: In the past decade, crowd counting and localization have gained much attention from researchers due to their wide range of applications. The NWPU-Crowd dataset is constructed to address the issue of small-scale datasets, containing a large number of annotated heads with points and boxes. A benchmark website is developed for impartial evaluation of different methods, providing researchers with a platform to submit test results and analyze new challenges in the field.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhiheng Ma et al.
Summary: By proposing scale alignment and a loss function based on efficient sliced Wasserstein distance, we have achieved universal crowd counting model learning across scenes and datasets with better generalizability.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Qingyu Song et al.
Summary: This paper introduces a purely point-based framework for joint crowd counting and individual localization, with a new performance evaluation metric. By directly predicting point proposals with Point to Point Network (P2PNet), which is consistent with human annotations, it achieves significant improvement over existing methods.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Yanyu Xu et al.
Summary: This paper studies a novel crowd counting setting by using only partial annotations in each image as training data. With a network consisting of three components, the model effectively tackles unannotated regions and achieves better performance on several datasets compared to recent methods and baselines.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Feng Dai et al.
Summary: This paper proposes a network model called DSNet for crowd counting, which uses dense dilated convolution blocks to preserve information from different scales and expands the scale range by dense residual connections. Through a novel loss function, DSNet achieves the best performance on five crowd counting datasets, demonstrating significant improvements in crowd counting accuracy.
PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL (ICMR '21)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Liangzi Rong et al.
Summary: This paper introduces a novel method CFANet which generates high-quality crowd density maps and people count estimation by incorporating attention maps to better focus on the crowd area. The method utilizes a progressive attention mechanism to suppress irrelevant background and assign attention weights based on crowd density levels, achieving superior performance in count accuracy and image quality improvement. Through multi-level supervision and a Background-aware Structural Loss, the method also reduces false recognition ratio while enhancing structural similarity to groundtruth.
2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Pongpisit Thanasutives et al.
Summary: This paper introduces two modified neural network models for accurate and efficient crowd counting. These models, named M-SFANet and M-SegNet, are based on SFANet and SegNet respectively, with enhancements in multi-scale feature extraction and context information integration, resulting in improved counting performance.
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
(2021)
Article
Computer Science, Artificial Intelligence
Deepak Babu Sam et al.
Summary: A novel detection framework for dense crowd counting is proposed, eliminating the need for density regression paradigm by locating and counting every person accurately. Challenges in designing such a system include diverse dense crowds and contiguous box prediction, which are successfully addressed by the LSC-CNN model.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Jia Liu et al.
Summary: This paper conducts a comprehensive analysis of crowd evacuation research using reference co-citation analysis, author co-citation analysis, and keyword co-occurrence analysis. It identifies key issues and future research directions in the field of crowd evacuation.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2021)
Article
Computer Science, Information Systems
Lei Liu et al.
Summary: This paper introduces a network called DENet, consisting of a detection network and an encoder-decoder estimation network, for counting people or objects with varying scales and densities. Evaluation on three datasets shows that DENet achieves lower Mean Absolute Error (MAE) than state-of-the-art methods.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Information Systems
Xiaoheng Jiang et al.
Summary: In this study, a novel density-aware convolutional neural network (DensityCNN) method is proposed to perform crowd counting by learning density-level classification and density map estimation. Extensive experiments demonstrate the high effectiveness of the proposed method across multiple datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Multidisciplinary Sciences
Sultan Daud Khan et al.
Summary: Head detection-based crowd counting is crucial for various visual applications, but limited work has been done on detecting human heads in high-density crowds. This paper proposes SS-CNN and DS-CNN networks to address this issue, achieving superior performance on challenging datasets.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Yifan Yang et al.
Summary: The study presents a simple yet effective multi-column network that integrates perspective analysis method with counting network, enabling efficient analysis of crowd quantities in scenes of various scales. By sharing parameters and introducing strategies like transform dilated convolution, the evaluation accuracy is improved, achieving state-of-the-art performance on multiple datasets.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Jia Wan et al.
Summary: This article introduces a fine-grained crowd counting method, which categorizes crowds and counts the number of individuals in each category, suitable for practical applications. By constructing a new fine-grained counting dataset, proposing a two branch architecture, and two optimization strategies, the algorithm's prediction accuracy is effectively improved.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Yi Wang et al.
Summary: The article introduces a novel self-training approach named Crowd-SDNet that enables a typical object detector trained only with point-level annotations to estimate the center points and sizes of crowded objects. The method significantly improves object detection and counting capabilities and achieves outstanding results on multiple benchmark datasets.
IEEE TRANSACTIONS ON IMAGE PROCESSING
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Jiwei Chen et al.
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Xingjiao Wu et al.
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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
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2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
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2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
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INTERNATIONAL JOURNAL OF COMPUTER VISION
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2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
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Ross Girshick
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
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IEEE TRANSACTIONS ON IMAGE PROCESSING
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EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING
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