Beibei Zhan

Crowd analysis: a survey

In the year 1999 the world population reached 6 billion, doubling the previous census estimate of 1960. Recently, the United States Census Bureau issued a revised forecast for world population showing a projected growth to 9.4 billion by 2050 (US Census Bureau, http://www.census.gov/ipc/www/worldpop.html). Different research disci- plines have studied the crowd phenomenon and its dynamics from a social, psychological and computational standpoint respectively. This paper presents a survey on crowd analysis methods employed in computer vision research and discusses perspectives from other research disciplines and how they can contribute to the computer vision approach.

Mining paths of complex crowd scenes

The Ambient Intelligence (AmI) paradigm requires a robust interpretation of people actions and behaviour and a way for automatically generating persistent spatial-temporal models of recurring events. This paper describes a relatively inexpensive technique that does not require the use of conventional trackers to identify the main paths of highly cluttered scenes, approximating them with spline curves. An AmI system could easily make use of the generated model to identify people who do not follow prefixed paths and warn them. Security, safety, rehabilitation are potential application areas. The model is evaluated against new data of the same scene.

Motion estimation with edge continuity constraint for crowd scene analysis

This paper presents a new motion estimation method aimed at crowd scene analysis in complex video sequences. The proposed technique makes use of image descriptors extracted from points lying at the maximum curvature on the Canny edge map of an analyzed image. Matches between two consecutive frames are then carried out by searching for descriptors that satisfy both a well-defined similarity metric and a structural constraint imposed by the edge map. A preliminary assessment using real-life video sequences gives both qualitative and quantitative results.

The analysis of crowd dynamics: From observations to modeling

Crowd is a familiar phenomenon studied in a variety of research disciplines including sociology, civil engineering and physics. Over the last two decades computer vision has become increasingly interested in studying crowds and their dynamics: because the phenomenon is of great scientific interest, it offers new computational challenges and because of a rapid increase in video surveillance technology deployed in public and private spaces. In this chapter computer vision techniques, combined with statistical methods and neural network, are used to automatically observe measure and learn crowd dynamics. The problem is studied to offer methods to measure crowd dynamics and model the complex movements of a crowd. The refined matching of local descriptors is used to measure crowd motion and statistical analysis and a kind of neural network, self-organizing maps were employed to learn crowd dynamics models.

Self-Organizing Maps for the Automatic Interpretation of Crowd Dynamics

This paper introduces the use of self-organizing maps for the visualization of crowd dynamics and to learn models of the dominant motions of crowds in complex scenes. The self-organizing map (SOM) model is a well known dimensionality reduction method proved to bear resemblance with characteristics of the human brain, representing sensory input by topologically ordered computational maps. This paper proposes algorithms to learn and compare crowd dynamics with the SOM model. Different information is employed as input to the used SOM. Qualitative and quantitative results are presented in the paper.

A quantitative comparison of two new motion estimation algorithms

This paper proposes a comparison of two motion estimation algorithms for crowd scene analysis in video sequences. The first method uses the local gradient supported by neighbouring topology constraints. The second method makes use of descriptors extracted from points lying at the maximum curvature along Canny edges. Performance is evaluated using real-world video sequences, providing the reader with a quantitative comparison of the two methods.