Titlebook: Emotion, Personality and Cultural Aspects in Crowds; Towards a Geometrica Rodolfo Migon Favaretto,Soraia Raupp Musse,Angelo Book 2019 Spri

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Kindle

Crowds and Groups of Peoplend presents the concept of ., which deals with interpersonal distances, which may vary across cultures. Also it presents the fundamental diagrams concept, describing the relationship between speed, density, and flow of pedestrians in a crowd, which may also be influenced by cultural aspects.

Dislocation

Tracking and Data Extractionter that, homogeneous planar projection is performed in order to obtain the parameters in world coordinates (once the trajectories obtained in the tracking process are in image coordinates). Section 5.3 describes the detection of groups and the extraction of their information.

無(wú)能力

Final Remarksnd crowd characteristics, regarding personalities, emotions, and cultural aspects. The main goal of this research was to propose a model that could detect pedestrian features in crowds from video sequences, allowing the comparison and analysis of pedestrian characteristics from different crowds or countries.

政府

https://doi.org/10.1007/978-3-319-73845-1re would probably be differences masked by the features of the places where the videos were recorded. In addition, this chapter presents an approach to detect the crowd types described in Sect. 2.3. The type of the crowd is used to compute the video similarity.

Androgen

Androgen

Video Similarity and Crowd Typesre would probably be differences masked by the features of the places where the videos were recorded. In addition, this chapter presents an approach to detect the crowd types described in Sect. 2.3. The type of the crowd is used to compute the video similarity.

SLING

Video Analysis Dataset and Applications from various countries. We show how the dataset is organized. Section 10.2 is responsible for present ., a software developed in MATLAB App Designer with a simple interface to detect and analyze the geometrical dimensions in videos. Lastly, Sect. 10.3 presents a crowd features viewer.

Stagger

Migratory

Linear Models for Outlier Detection,yze and discuss some results from the FD experiment. Section 9.3 addresses an analysis involving personal space in a comparison with Sorokowska et al. (J Cross-Cultural Psychol 48:577–592, 2017). Finally, Sect. 9.4 presents an analysis regarding personality and emotion traits in the fundamental diagram.