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Quantifying human mobility resilience to extreme events using geo-located social media data EPJ Data Sci., 8 (2019), p. 18 View in ScopusGoogle Scholar Ryan et al., 2009 Ryan, D., Denman, S., Fookes, C., Sridharan, S., 2009. Crowd counting using multiple local features. In: Digital...
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This additional information cue consists of modeling time-varying dynamics of the crowd density using local features as an observation of a probabilistic function. It also involves a feature tracking step which allows excluding feature points attached to the background. This process is favorable for ...
Multiple CNN columns with receptive fields of different sizes are designed in these architectures to extract features at different scales. More recently, researchers have further improved the performance of crowd counting by deepening the network to enlarge the receptive field [10] or generating high-...
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets 2021, IEEE Transactions on Image Processing Quantifying and Detecting Collective Motion in Crowd Scenes 2020, IEEE Transactions on Image Processing Physics inspired methods for crowd video surveillance and analysis...
Crowd counting is a very difficult task due to the presence of cluttered backgrounds in crowd scenes. Although recent counting algorithms have achieved gre
Existing crowd counting algorithms rely on holistic, local or histogram based features to capture crowd properties. Regression is then employed to estimate the crowd size. Insufficient testing across multiple datasets has made it difficult to compare and contrast different methodologies. This paper present...