Essay On Crowd Counting

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2.2.1 Crowd Analysis Techniques Based on Computer Vision Computer vision methods have been widely used to study different aspects of crowd analysis. Jacques, Musse and Jung (2010) discussed three aspects: crowd counting/density estimation, tracking in crowded sights and high-level crowd behavior understanding. Crowd counting Crowd counting is one of the fundamental issues in the area of crowd analysis as counting assists in more efficient crowd management such as overcrowding anticipation and managing future crowds. One of the famous counting approaches is counting-by-detection, which uses a trained detector using local features (e.g. histogram oriented gradients [HOG] or Haar wavelets) to locate individuals in the scene
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Dehghan et al. (2013) presented a method to handle fully or partial occlusion by preforming part based detection, but instead of taking the score of all the parts it considers the detection of full body, upper body and head only. To form the trajectories of tracking, a video is divided into segments in order to find people tracklets, and then merge them to find the trajectories using Generalized Minimum Clique Problem (GMCP). Crowd behavior understanding
Studying the actions of a crowd is a significant topic in computer vision and still under research. crowd behavior serves a great variety of such as detecting abnormal motions, crowd direction and speed which used further to predict the behavior in a specific environment at a particular time to help in a better crowd management. two main approaches are used; object-based and holistic. object-based divides the crowd into segments and stay the behavior of the individuals in the crowd. For example, a man opposing the crowd direction is an indication of a dangerous situation. While holistic methods treat the crowd as a whole and try to conclude global information which makes it suites the high-density crowds (Jacques, Musse and Jung

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