The Experts below are selected from a list of 17310 Experts worldwide ranked by ideXlab platform
Jiaya Jia - One of the best experts on this subject based on the ideXlab platform.
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l_0 regularized stationary time estimation for Crowd Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017Co-Authors: Xiaogang Wang, Jiaya JiaAbstract:In this paper, we tackle the problem of stationary Crowd Analysis which is as important as modeling mobile groups in Crowd scenes and finds many important applications in Crowd surveillance. Our key contribution is to propose a robust algorithm for estimating how long a foreground pixel becomes stationary. It is much more challenging than only subtracting background because failure at a single frame due to local movement of objects, lighting variation, and occlusion could lead to large errors on stationary-time estimation. To achieve robust and accurate estimation, sparse constraints along spatial and temporal dimensions are jointly added by mixed partials (which are second-order gradients) to shape a 3D stationary-time map. It is formulated as an $L_0$ optimization problem. Besides background subtraction, it distinguishes among different foreground objects, which are close or overlapped in the spatio-temporal space by using a locally shared foreground codebook. The proposed technologies are further demonstrated through three applications. 1) Based on the results of stationary-time estimation, 12 descriptors are proposed to detect four types of stationary Crowd activities. 2) The averaged stationary-time map is estimated to analyze Crowd scene structures. 3) The result of stationary-time estimation is also used to study the influence of stationary Crowd groups to traffic patterns.
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l0 regularized stationary time estimation for Crowd group Analysis
Computer Vision and Pattern Recognition, 2014Co-Authors: Xiaogang Wang, Jiaya JiaAbstract:We tackle stationary Crowd Analysis in this paper, which is similarly important as modeling mobile groups in Crowd scenes and finds many applications in surveillance. Our key contribution is to propose a robust algorithm of estimating how long a foreground pixel becomes stationary. It is much more challenging than only subtracting background because failure at a single frame due to local movement of objects, lighting variation, and occlusion could lead to large errors on stationary time estimation. To accomplish decent results, sparse constraints along spatial and temporal dimensions are jointly added by mixed partials to shape a 3D stationary time map. It is formulated as a L0 optimization problem. Besides background subtraction, it distinguishes among different foreground objects, which are close or overlapped in the spatio-temporal space by using a locally shared foreground codebook. The proposed technologies are used to detect four types of stationary group activities and analyze Crowd scene structures. We provide the first public benchmark dataset1 for stationary time estimation and stationary group Analysis.
Xiaogang Wang - One of the best experts on this subject based on the ideXlab platform.
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l_0 regularized stationary time estimation for Crowd Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017Co-Authors: Xiaogang Wang, Jiaya JiaAbstract:In this paper, we tackle the problem of stationary Crowd Analysis which is as important as modeling mobile groups in Crowd scenes and finds many important applications in Crowd surveillance. Our key contribution is to propose a robust algorithm for estimating how long a foreground pixel becomes stationary. It is much more challenging than only subtracting background because failure at a single frame due to local movement of objects, lighting variation, and occlusion could lead to large errors on stationary-time estimation. To achieve robust and accurate estimation, sparse constraints along spatial and temporal dimensions are jointly added by mixed partials (which are second-order gradients) to shape a 3D stationary-time map. It is formulated as an $L_0$ optimization problem. Besides background subtraction, it distinguishes among different foreground objects, which are close or overlapped in the spatio-temporal space by using a locally shared foreground codebook. The proposed technologies are further demonstrated through three applications. 1) Based on the results of stationary-time estimation, 12 descriptors are proposed to detect four types of stationary Crowd activities. 2) The averaged stationary-time map is estimated to analyze Crowd scene structures. 3) The result of stationary-time estimation is also used to study the influence of stationary Crowd groups to traffic patterns.
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l0 regularized stationary time estimation for Crowd group Analysis
Computer Vision and Pattern Recognition, 2014Co-Authors: Xiaogang Wang, Jiaya JiaAbstract:We tackle stationary Crowd Analysis in this paper, which is similarly important as modeling mobile groups in Crowd scenes and finds many applications in surveillance. Our key contribution is to propose a robust algorithm of estimating how long a foreground pixel becomes stationary. It is much more challenging than only subtracting background because failure at a single frame due to local movement of objects, lighting variation, and occlusion could lead to large errors on stationary time estimation. To accomplish decent results, sparse constraints along spatial and temporal dimensions are jointly added by mixed partials to shape a 3D stationary time map. It is formulated as a L0 optimization problem. Besides background subtraction, it distinguishes among different foreground objects, which are close or overlapped in the spatio-temporal space by using a locally shared foreground codebook. The proposed technologies are used to detect four types of stationary group activities and analyze Crowd scene structures. We provide the first public benchmark dataset1 for stationary time estimation and stationary group Analysis.
Kaiqi Huang - One of the best experts on this subject based on the ideXlab platform.
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Gestalt laws based tracklets Analysis for human Crowd understanding
Pattern Recognition, 2018Co-Authors: Weiqi Zhao, Zhang Zhang, Kaiqi HuangAbstract:Crowded scene Analysis is a popular research topic due to its great application potentials, such as intelligent video surveillance and Crowd density estimation. In this paper, we propose a novel approach to detecting Crowd groups and learning semantic regions with a unified hierarchical clustering framework. According to the Gestalt laws of grouping, we propose three priors to define a unified similarity metric to measure the similarities of pairs of original tracklets and pairs of representative tracklets from different Crowd groups, so that the short-term Crowd groups and the long-term semantic paths commonly composed of several short-term Crowd groups can be detected by a bottom-up hierarchical clustering algorithm simultaneously. In order to verify our method at the longer time duration video sequences in the Crowded scene, we construct a new Crowd database (CASIA Crowd database1) with various Crowd densities in real scenes. Extensive experiments on our CASIA Crowd database, Collective Motion Database and CUHK database are performed, and the results demonstrate that our approach is effective and reliable for Crowd detection and semantic scene understanding in various Crowd densities, especially for the Crowd Analysis in long temporal video clips.
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large scale Crowd Analysis based on convolutional neural network
Pattern Recognition, 2015Co-Authors: Lijun Cao, Xu Zhang, Weiqiang Ren, Kaiqi HuangAbstract:Nowadays Crowd surveillance is an active area of research. Crowd surveillance is always affected by various conditions, such as different scenes, weather, or density of Crowd, which restricts the real application. This paper proposes a convolutional neural network (CNN) based method to monitor the number of Crowd flow, such as the number of entering or leaving people in high density Crowd. It uses an indirect strategy of combining classification CNN with regression CNN, which is more robust than the direct way. A large enough database is built with lots of real videos of public gates, and plenty of experiments show that the proposed method performs well under various weather conditions no matter either in daytime or at night. HighlightsA method to estimate the number of Crowd flow with CNN models is proposed.A database with 140 thousand samples from real scenes is build.The experiments perform robust under various scenes, weather or Crowded condition.
Haidi Ibrahim - One of the best experts on this subject based on the ideXlab platform.
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recent survey on Crowd density estimation and counting for visual surveillance
Engineering Applications of Artificial Intelligence, 2015Co-Authors: Sami Abdulla Mohsen Saleh, Shahrel Azmin Suandi, Haidi IbrahimAbstract:Automated Crowd density estimation and counting are popular and important topic in Crowd Analysis. The last decades witnessed different of many significant publications in this field and it has been and still a challenging problem for automatic visual surveillance over many years. This paper presents a survey on Crowd density estimation and counting methods employed for visual surveillance in the perspective of computer vision research. This survey covers two main approaches which are direct approach (i.e., object based target detection) and indirect approach (e.g. pixel-based, texture-based, and corner points based Analysis). This review categorizes and delineates several Crowd density estimation and counting methods that have been applied for the examination of Crowd scenes.
Mubarak Shah - One of the best experts on this subject based on the ideXlab platform.
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composition loss for counting density map estimation and localization in dense Crowds
European Conference on Computer Vision, 2018Co-Authors: Haroon Idrees, Muhmmad Tayyab, Kishan Athrey, Dong Zhang, Somaya Almaadeed, Nasir M Rajpoot, Mubarak ShahAbstract:With multiple Crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals; visual Crowd Analysis is emerging as a new frontier in computer vision. In particular, counting in highly dense Crowds is a challenging problem with far-reaching applicability in Crowd safety and management, as well as gauging political significance of protests and demonstrations. In this paper, we propose a novel approach that simultaneously solves the problems of counting, density map estimation and localization of people in a given dense Crowd image. Our formulation is based on an important observation that the three problems are inherently related to each other making the loss function for optimizing a deep CNN decomposable. Since localization requires high-quality images and annotations, we introduce UCF-QNRF dataset that overcomes the shortcomings of previous datasets, and contains 1.25 million humans manually marked with dot annotations. Finally, we present evaluation measures and comparison with recent deep CNNs, including those developed specifically for Crowd counting. Our approach significantly outperforms state-of-the-art on the new dataset, which is the most challenging dataset with the largest number of Crowd annotations in the most diverse set of scenes.
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composition loss for counting density map estimation and localization in dense Crowds
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Haroon Idrees, Muhmmad Tayyab, Kishan Athrey, Dong Zhang, Somaya Almaadeed, Nasir M Rajpoot, Mubarak ShahAbstract:With multiple Crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals; visual Crowd Analysis is emerging as a new frontier in computer vision. In particular, counting in highly dense Crowds is a challenging problem with far-reaching applicability in Crowd safety and management, as well as gauging political significance of protests and demonstrations. In this paper, we propose a novel approach that simultaneously solves the problems of counting, density map estimation and localization of people in a given dense Crowd image. Our formulation is based on an important observation that the three problems are inherently related to each other making the loss function for optimizing a deep CNN decomposable. Since localization requires high-quality images and annotations, we introduce UCF-QNRF dataset that overcomes the shortcomings of previous datasets, and contains 1.25 million humans manually marked with dot annotations. Finally, we present evaluation measures and comparison with recent deep CNN networks, including those developed specifically for Crowd counting. Our approach significantly outperforms state-of-the-art on the new dataset, which is the most challenging dataset with the largest number of Crowd annotations in the most diverse set of scenes.