Abstract
Intelligent Crowd Monitoring and Management Systems (ICMMSs) have become effective resources for strengthening safety and security along with enhancing early-warning capabilities to manage emergencies in crowded situations of smart cities and massive gatherings events. The main advantage of such systems is their ability to detect multiple features associated with the crowd gathering, as they enable multi-source sensors, multi-modal data, and powerful intelligent and analytical methods. Unlike traditional crowd monitoring systems, which make use of simplex forms of different data types, data and information associated with crowded scenarios can be collected, fused, processed and analyzed in large quantities for accurate global assessment and enhanced decision making processes in an ICMMS. Therefore, data fusion is introduced as an enabler to decrease data quantity, reduce data dimensions, and improve data quality. In this paper, we first survey the literature on data fusion application in crowd monitoring systems as we are developing a state-of-the-art ICMMS with data fusion as a major platform enabler. Next, we discuss some popular data fusion architectures and classifications from different perspectives. Based on this, we propose a multi-sensor, multi-modal, and dimensional ICMMS architecture based on data fusion. Then, we identify the data fusion processes in the ICMMS and classify them into sensor fusion, feature-based data fusion, and decision fusion. Relevant algorithms, applications and examples of three classes are elaborated. Finally, future data fusion research directions are discussed.
Highlights
With the development of sensor technology, communication technology, and big data science [1], smart city-oriented intelligent applications [2] have become important services in human life
DATA FUSION-BASED Intelligent Crowd Monitoring and Management Systems (ICMMSs) ARCHITECTURE Compared with a single-information-source system, ICMMSs with data fusion [46] have great advantages in many aspects, such as space coverage, monitoring time span, data redundancy, data source reliability, system robustness, data complexity, storage resources, computing performance requirements, and application services [47]
We propose a multi-sensor, multimodal, and multi-dimension ICMMS based on a three-layer data fusion structure including sensor fusion, feature-based data fusion and decision fusion
Summary
With the development of sensor technology, communication technology, and big data science [1], smart city-oriented intelligent applications [2] have become important services in human life. With the development of monitoring technology, vision-based crowd monitoring technology has experienced the following development stages: 1) in ‘‘one-to-one’’ monitoring, the monitor corresponds to CCTV one-by-one Devices in this mode are fixed and inflexible; 2) in circuit switching for monitoring, wiring and operation are complex, while network expansion performance is poor; and 3) in multimedia monitoring, the video can be switched smoothly while the visualization can be well controlled. 4) There is insufficient timeliness and intelligence for decision-making Some abnormal incidents, such as crowding, trampling, fights, fire, hail, and violence attacks, depend on real-time monitoring and a high-efficiency crowd evacuation and management mechanism using artificial intelligence (AI) and communication technologies. As mentioned above, there are many isometric sensors in an ICMMS This means the system analyzes a large quantity of multi-source and multi-modal data deeply and accurately in real time making an effective and intelligent decision in a short time.
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