A Literature Review on Video Analytics of Crowded Scenes
This chapter presents a review and systematic comparison of the state of the art on crowd video analysis. The rationale of our review is justified by a recent increase in intelligent video surveillance algorithms capable of analysing automatically visual
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Abstract This chapter presents a review and systematic comparison of the state of the art on crowd video analysis. The rationale of our review is justified by a recent increase in intelligent video surveillance algorithms capable of analysing automatically visual streams of very crowded and cluttered scenes, such as those of airport concourses, railway stations, shopping malls and the like. Since the safety and security of potentially very crowded public spaces have become a priority, computer vision researchers have focused their research on intelligent solutions. The aim of this chapter is to propose a critical review of existing literature pertaining to the automatic analysis of complex and crowded scenes. The literature is divided into two broad categories: the macroscopic and the microscopic modelling approach. The effort is meant to provide a reference point for all computer vision practitioners currently working on crowd analysis. We discuss the merits and weaknesses of various approaches for each topic and provide a recommendation on how existing methods can be improved.
M. Thida (B) · H.-l. Eng ZWEEC Analytics, Singapore, Singapore e-mail: [email protected] H.-l. Eng e-mail: [email protected] Y.L. Yong · P. Climent-Pérez · P. Remagnino Kingston University, London, UK Y.L. Yong e-mail: [email protected] P. Climent-Pérez e-mail: [email protected] P. Remagnino e-mail: [email protected] P.K. Atrey et al. (eds.), Intelligent Multimedia Surveillance, DOI 10.1007/978-3-642-41512-8_2, © Springer-Verlag Berlin Heidelberg 2013
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1 Introduction Automated video content analysis of a crowded scene has been an active research area in the field of computer vision in the last few years. This strong interest is driven by the increased demand for public safety at crowded spaces such as airports, train stations, malls, stadiums, etc. In such scenes, conventional computer vision techniques for video surveillance cannot be directly applied in the crowded scene due to large variations of crowd densities, complex crowd dynamics and severe occlusions in the scene. Algorithms for people detection, tracking and activity analysis which consider an individual in isolation (i.e., individual object segmentation and tracking) often face difficult situations such as the overlapping of pedestrians, complex events due to interactions among pedestrians in a crowd. For this reason, many papers consider the crowd as a single entity and analyse its dynamics. The status of crowd is updated as normal or abnormal based on the dynamics of the whole crowd. However, a crowded condition can also be unstructured where pedestrians are relatively free to move in many directions as opposed to a structured crowd where each individual moves coherently in one common direction. In an unstructured crowded scene, considering the crowd as one entity will fail to identify abnormal events which arise due to an inappropriate action of an individual in a crowd. For instance, a running person in a crowd can indicate a
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