Spectator Crowd Research Articles

Action recognition in poor-quality spectator crowd videos using head distribution-based person segmentation

Despite a big volume of research on action recognition, little attention has been given to individual action recognition in poor-quality spectator crowd scenes. It is an important scenario, because most of the surveillance systems generate poor-quality videos, though current state-of-the-art methods may not be effectively applicable. Therefore recognizing actions performed by individuals in poor-quality spectator crowd scenes is an unsolved problem. In such cases, the main challenge is localizing person proposals for each actor in the crowd. This challenge becomes more difficult when occlusion is severe. In this work, we propose a novel approach to find person proposals in poor-quality spectator crowds using crowd-based constraints. First, we define persons in the crowd by using efficient person head detectors. We exploit person head size to estimate the person bounding box using linear regression. Then, we use distribution of heads in the crowd image to estimate more accurate person proposals. Motion trajectories are independently computed in the video without considering persons and then assigned to each person based on a novel distance measure computed between the trajectory and the person proposal. The set of trajectories and associated motion and texture-based features in overlapped time windows are used to compute the final feature vector. For each time window using early information fusion in the bag of visual-words framework, cumulative feature vectors are computed encoding action information. Experiments are performed on a publicly available real-world spectator crowd dataset containing as many as 150 actors performing multiple actions at the same time. Our experiments have demonstrated excellent performance of the proposed technique.

The S-Hock dataset: A new benchmark for spectator crowd analysis

Although crowd analysis is a classical and extensively studied problem for the computer vision community, the vast majority of the works in the literature assume a single type of crowd, while the sociological literature classifies a number of different typologies, each one with their own distinctive traits. In this paper we focus on a particular kind of crowd referred in sociology as spectator crowd, which consists a number of people that are “interested in watching something specific that they came to see” Berlonghi (1995). This is the typical social formation that attends entertainment events like sport matches, concerts, movies, etc. In this work we present a novel dataset, the Spectators Hockey (S-Hock), containing almost 30 hours of videos recorded at an ice hockey rink during the Winter Universiade “Trentino2013”. On these data we provide a massive annotation, focusing on the spectators at different levels of detail: from high level features describing which team a person supports and if he/she knows his/her neighbors; to a lower level, where we consider standard pose information as well as atomic actions like applauding, jumping, etc. We also provide annotations for the game field, which allows us to analyze the relationship between the crowd behavior and the events of the match. Eventually we provide more than 100 million of annotations, that can be used for many different tasks spanning from standard applications, like people counting and head pose estimation, to higher level tasks, like excitement estimation and automatic summarization. We provide protocols and baseline results for all of these applications, encouraging further research in these field.

Nationalism and the crowd in liberal Hungary, 1848-1914

Hungary's revolutionary crowd of 1848 was defeated in 1849, but crowds of other kinds and crowd politics remained central to Hungary as it fashioned itself over the next half-century. Nationalism and the Crowd in Liberal Hungary, 1848-1914, describes how the crowd's shifting cast of characters participated in the making of Hungary inside the increasingly troubled Austro-Hungarian empire. Audiences at theaters, fairs, statue raisings, and commemorations of national figures; political rallies; ethnic mobs; May Day celebrations; monarchical festivities; and finally war rallies all take up places in this history. Not only insurgent crowds, but festive ones as well have political and material goals, Freifeld finds. Parading before a spectator crowd may have confirmed noble participants in their claims to be spokespersons of the nation, but the chastened crowd could also feel its presence was instrumental, she writes. Even as the chastened crowd became an instrument to advance the elite's agenda by rallying support within the nation, it was never a slave to the leaders on the podium or simply manipulated by them, for it, too, demanded deference from its pageant masters. And hope for liberal nationalism, which Hungarian crowds carried from their experience of 1848, thus continued to confront the monarchy, its bureaucracy, and the gentry. The book is an imaginative contribution to the research in nationalism, liberalism, and the crowd, as well.