Emotion estimation in crowds: a survey <s> Crowd Analysis Datasets <s> This paper presents an evaluation on the reliability of the bench-mark datasets for outdoor crowd analytic surveillance systems. The credibility of the databases are assessed based on their diverseness to yield challenges of dynamic environments. The main object of this paper is to assess the challenges imposed by the databases for sudden illumination variance and effect of wavering trees. Two bench-mark databases, PETS 2010 and OTCBVS, along with our proposed dataset are evaluated using the three most popular background modelling algorithms in crowd analytic surveillance; Approximate Median Method, Gaussian Mixture Model and Codebook. The diverseness of these databases are assessed, with respect to the performance of the basic algorithms using qualitatively and quantitatively. Eventually the reliability of these bench-mark databases for outdoor crowed analytic surveillance is assessed. <s> BIB001 </s> Emotion estimation in crowds: a survey <s> Crowd Analysis Datasets <s> Understanding group activities is an essential step towards studying complex crowd behaviours in video. However, such research is often hampered by the lack of a formal definition of a group, as well as a dearth of datasets that concentrate specifically on Atomic Group Actions. 1 In this paper, we provide a quantitative definition of a group based on the Group Transition Ratio (Gtr); the Gtr helps determine when individuals transition to becoming a group (where the individuals can still be tracked) or a crowd (where tracking of individuals is lost). In addition, we introduce the Atomic Group Actions Dataset, a set of 200 videos that concentrate on the atomic group actions of objects in video, namely the group-group actions of formation, dispersal, and movement of a group, as well as the group-person actions of person joining and person leaving a group. We further incorporate a structured, end-to-end analysis methodology, based on workflows, to easily and automatically allow for standardized testing of new group action models against this dataset. We demonstrate the efficacy of the Gtr on the Atomic Group Actions Dataset and make the full dataset (the videos, along with their associated tracks and ground truth, and the exported workflows) publicly available to the research community for free use and extension at at http://research. sethi.org/ricky/datasets/. <s> BIB002 </s> Emotion estimation in crowds: a survey <s> Crowd Analysis Datasets <s> Crowd understanding has drawn increasing attention from the computer vision community, and its progress is driven by the availability of public crowd datasets. In this paper, we contribute a large-scale benchmark dataset collected from the Shanghai 2010 World Expo. It includes $2630$ annotated video sequences captured by $245$ surveillance cameras, far larger than any public dataset. It covers a large number of different scenes and is suitable for evaluating the performance of crowd segmentation and estimation of crowd density, collectiveness, and cohesiveness, all of which are universal properties of crowd systems. In total, $53\,637$ crowd segments are manually annotated with the three crowd properties. This dataset is released to the public to advance research on crowd understanding. The large-scale annotated dataset enables using data-driven approaches for crowd understanding. In this paper, a data-driven approach is proposed as a baseline of crowd segmentation and estimation of crowd properties for the proposed dataset. Novel global and local crowd features are designed to retrieve similar training scenes and to match spatio-temporal crowd patches so that the labels of the training scenes can be accurately transferred to the query image. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art approaches for crowd understanding. <s> BIB003 </s> Emotion estimation in crowds: a survey <s> Crowd Analysis Datasets <s> Despite the huge research on crowd on behavior understanding in visual surveillance community, lack of publicly available realistic datasets for evaluating crowd behavioral interaction led not to have a fair common test bed for researchers to compare the strength of their methods in the real scenarios. This work presents a novel crowd dataset contains around 45,000 video clips which annotated by one of the five different fine-grained abnormal behavior categories. We also evaluated two state-of-the-art methods on our dataset, showing that our dataset can be effectively used as a benchmark for fine-grained abnormality detection. The details of the dataset and the results of the baseline methods are presented in the paper. <s> BIB004
The fields of computer vision and crowd analysis are favored with an overgrowing importance and share a common interest in studying crowded environments, and as a result, multiple datasets have been produced. In compiling such datasets, cameras remain to be the preferred sensor for studying crowds due to the already widespread use of surveillance cameras in most public spaces. Depending on the focus of study, datasets are designed to capture the desired circumstances. The popular dataset PETS 2009  collected image sequences from multiple cameras with the aim to serve as a test bed for algorithms intended for people counting, density estimation, people tracking, flow analysis and event recognition. All the presented situations are mainly poor in terms of emotional behavior, except for the scenario S3 (event recognition) where an evacuation (rapid dispersion) is observed and can be associated to an emotional state of fear. The authors of CAD BIB001 recreated several normal collective behaviors adding the challenges of change in illumination and wavering trees in the background, however, the captured situations are not representative of any emotional behavior. Taking advantage of the large number of people attending the World Exposition of 2010 in Shanghai, the massive dataset Shanghai Expo 10 BIB003 was gathered. It provides a large amount of annotations at a regional level denoting crowd density, collectiveness and cohesiveness features under normal situations, but it lacks any relevance for inferring affective states. Focusing on groups and crowds, the authors of BIB002 present the Atomic Group Action dataset targeting the dynamics of group formation, yet no meaningful emotional behavior is exhibit. Rabiee's dataset BIB004 provides some emotional-rich situations such as panic and fight, although in a staged way. Finally, the S-hock dataset  focuses on the behavior of spectator crowds with rich annotations at the individual level, enabling the addition of further affective annotations although restricted to this type of crowds.