Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In...Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.展开更多
Many online platforms providing crowd with opportunities to participate in software development projects have been existed for a while. Meanwhile, many enterprises are using crowd source to collaboratively develop the...Many online platforms providing crowd with opportunities to participate in software development projects have been existed for a while. Meanwhile, many enterprises are using crowd source to collaboratively develop their software via these platforms in recent years. However, some software development projects in these platforms hardly attract users to join. Therefore, these project owners need a way to effectively predict the number of participants in their projects and accordingly well plan their software and project specifications, such as the program language and the size of the documentation, in order to attract more individuals to participant in the projects. Compared with the past prediction models, our proposed model can dynamically add the factors, such as number of participants in the initial stage of the project, within the project life cycle and make the adjustment to the prediction model. The proposed model was also verified by using cross validation method. The results show that: 1) The models with the factor “the number of user participation” is more accurate than the model without it. 2) The factors of crowd dimension are more influential on the prediction accuracy than those of software project and owner dimensions. It is suggested that the project owners not only just consider those factors of the software project dimension in the initial stage of the project life cycle but also those factors of crowd and interaction dimensions in the late stage to attract more participants in their projects.展开更多
Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the future.In practice,emergency applications often require less trai...Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the future.In practice,emergency applications often require less training time.However,there is a little work on how to obtain good prediction performance with less training time.In this paper,we propose a simplified deep residual network for our problem.By using the simplified deep residual network,we can obtain not only less training time but also competitive prediction performance compared with the existing similar method.Moreover,we adopt the spatio-temporal attention mechanism to further improve the simplified deep residual network with reasonable additional time cost.Based on the real datasets,we construct a series of experiments compared with the existing methods.The experimental results confirm the efficiency of our proposed methods.展开更多
Crowd flow prediction has become a strategically important task in urban computing,which is the prerequisite for traffic management,urban planning and public safety.However,due to variousness of crowd flows,multiple h...Crowd flow prediction has become a strategically important task in urban computing,which is the prerequisite for traffic management,urban planning and public safety.However,due to variousness of crowd flows,multiple hidden correlations among urban regions affect the flows.Besides,crowd flows are also influenced by the distribution of Points-of-Interests(POIs),transitional functional zones,environmental climate,and different time slots of the dynamic urban environment.Thus,we exploit multiple correlations between urban regions by considering the mentioned factors comprehensively rather than the geographical distance and propose multi-graph convolution gated recurrent units(MGCGRU)for capturing these multiple spatial correlations.For adapting to the dynamic mobile data,we leverage multiple spatial correlations and the temporal dependency to build an urban flow prediction framework that uses only a little recent data as the input but can mine rich internal modes.Hence,the framework can mitigate the influence of the instability of data distributions in highly dynamic environments for prediction.The experimental results on two real-world datasets in Shanghai show that our model is superior to state-of-the-art methods for crowd flow prediction.展开更多
密集场景下准确人群计数和定位,对于保障公共安全具有重要的意义。针对密集人群计数与定位易受人群分布不均、背景干扰等因素的影响,导致计数定位不准确的问题,提出一种基于区域感知校准的自适应人群计数与定位方法。通过构建金字塔结...密集场景下准确人群计数和定位,对于保障公共安全具有重要的意义。针对密集人群计数与定位易受人群分布不均、背景干扰等因素的影响,导致计数定位不准确的问题,提出一种基于区域感知校准的自适应人群计数与定位方法。通过构建金字塔结构提取人群图像的多尺度特征,增强特征关联性,并设计可变形几何自适应模块学习不同分布的人群几何特征,以增强对人群分布不均的适应性。在此基础上,提出区域感知和区域校准模块,提取全局上下文特征和区域特征,克服了背景干扰造成的定位与计数不准问题。接着通过双分支卷积预测通路,输出生成点的预测位置和置信度分数,以提高网络的定位与计数精度。最后提出改进二分图最大匹配Hopcroft-Karp算法对真值点与预测点进行匹配校准,从而完成人群定位与计数。实验结果表明,所提方法分别在公开的ShanghaiTech Part A和Part B数据集、NWPU-Crowd数据集、UCF-QNRF数据集上评价指标均优于对比算法,且定位精度较P2Pnet分别提高了3.5%、6.1%、11.3%和8.1%,能够有效提高人群定位与计数的准确度。展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 61175007the National Key Technologies R&D Program under Grant No. 2012BAH07B01the National Key Basic Research Program of China (973 Program) under Grant No. 2012CB316302
文摘Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.
文摘Many online platforms providing crowd with opportunities to participate in software development projects have been existed for a while. Meanwhile, many enterprises are using crowd source to collaboratively develop their software via these platforms in recent years. However, some software development projects in these platforms hardly attract users to join. Therefore, these project owners need a way to effectively predict the number of participants in their projects and accordingly well plan their software and project specifications, such as the program language and the size of the documentation, in order to attract more individuals to participant in the projects. Compared with the past prediction models, our proposed model can dynamically add the factors, such as number of participants in the initial stage of the project, within the project life cycle and make the adjustment to the prediction model. The proposed model was also verified by using cross validation method. The results show that: 1) The models with the factor “the number of user participation” is more accurate than the model without it. 2) The factors of crowd dimension are more influential on the prediction accuracy than those of software project and owner dimensions. It is suggested that the project owners not only just consider those factors of the software project dimension in the initial stage of the project life cycle but also those factors of crowd and interaction dimensions in the late stage to attract more participants in their projects.
基金This work was supported by the National Nature Science Foundation of China(NSFC Grant Nos.61572537,U1501252).
文摘Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the future.In practice,emergency applications often require less training time.However,there is a little work on how to obtain good prediction performance with less training time.In this paper,we propose a simplified deep residual network for our problem.By using the simplified deep residual network,we can obtain not only less training time but also competitive prediction performance compared with the existing similar method.Moreover,we adopt the spatio-temporal attention mechanism to further improve the simplified deep residual network with reasonable additional time cost.Based on the real datasets,we construct a series of experiments compared with the existing methods.The experimental results confirm the efficiency of our proposed methods.
基金This work was supported by the National Natural Science Foundation of China under Grant No.61572253the Aviation Science Fund of China under Grant No.2016ZC52030.
文摘Crowd flow prediction has become a strategically important task in urban computing,which is the prerequisite for traffic management,urban planning and public safety.However,due to variousness of crowd flows,multiple hidden correlations among urban regions affect the flows.Besides,crowd flows are also influenced by the distribution of Points-of-Interests(POIs),transitional functional zones,environmental climate,and different time slots of the dynamic urban environment.Thus,we exploit multiple correlations between urban regions by considering the mentioned factors comprehensively rather than the geographical distance and propose multi-graph convolution gated recurrent units(MGCGRU)for capturing these multiple spatial correlations.For adapting to the dynamic mobile data,we leverage multiple spatial correlations and the temporal dependency to build an urban flow prediction framework that uses only a little recent data as the input but can mine rich internal modes.Hence,the framework can mitigate the influence of the instability of data distributions in highly dynamic environments for prediction.The experimental results on two real-world datasets in Shanghai show that our model is superior to state-of-the-art methods for crowd flow prediction.
文摘密集场景下准确人群计数和定位,对于保障公共安全具有重要的意义。针对密集人群计数与定位易受人群分布不均、背景干扰等因素的影响,导致计数定位不准确的问题,提出一种基于区域感知校准的自适应人群计数与定位方法。通过构建金字塔结构提取人群图像的多尺度特征,增强特征关联性,并设计可变形几何自适应模块学习不同分布的人群几何特征,以增强对人群分布不均的适应性。在此基础上,提出区域感知和区域校准模块,提取全局上下文特征和区域特征,克服了背景干扰造成的定位与计数不准问题。接着通过双分支卷积预测通路,输出生成点的预测位置和置信度分数,以提高网络的定位与计数精度。最后提出改进二分图最大匹配Hopcroft-Karp算法对真值点与预测点进行匹配校准,从而完成人群定位与计数。实验结果表明,所提方法分别在公开的ShanghaiTech Part A和Part B数据集、NWPU-Crowd数据集、UCF-QNRF数据集上评价指标均优于对比算法,且定位精度较P2Pnet分别提高了3.5%、6.1%、11.3%和8.1%,能够有效提高人群定位与计数的准确度。
