Moving Populations Event Recognition Under Re-Identification and Data Locality Constraints

For more than a decade tracking and tracing physical objects has been target of information systems within the realm of research on the Internet of Things. But application to human populations requires reconsideration of re-identification and data locality requirements due to ethical and legal constraints. For this domain, we propose a generic event recognition architecture (GERA) and evaluate its applicability for developing a sensor-based information system for recognizing moving population densities by obeying non-re-identification and data decentrality requirements. Empirical evaluations show that this information system provides mean structures for measuring event data and deriving predictions that are statistically equal to manually measured actual data. Finally, a general discussion on the integration of event recognition systems into busi-ness process environments is given

Crowd detection and counting using a static and dynamic platform: state of the art

Automated object detection and crowd density estimation are popular and important area in visual surveillance research. The last decades witnessed many significant research in this field however, it is still a challenging problem for automatic visual surveillance. The ever increase in research of the field of crowd dynamics and crowd motion necessitates a detailed and updated survey of different techniques and trends in this field. This paper presents a survey on crowd detection and crowd density estimation from moving platform and surveys the different methods employed for this purpose. This review category and delineates several detections and counting estimation methods that have been applied for the examination of scenes from static and moving platforms

Scene invariant crowd counting and crowd occupancy analysis

In public places, crowd size may be an indicator of congestion, delay, instability, or of abnormal events, such as a fight, riot or emergency. Crowd related information can also provide important business intelligence such as the distribution of people throughout spaces, throughput rates, and local densities.\ud \ud A major drawback of many crowd counting approaches is their reliance on large numbers of holistic features, training data requirements of hundreds or thousands of frames per camera, and that each camera must be trained separately. This makes deployment in large multi-camera environments such as shopping centres very costly and difficult.\ud \ud In this chapter, we present a novel scene-invariant crowd counting algorithm that uses local features to monitor crowd size. The use of local features allows the proposed algorithm to calculate local occupancy statistics, scale to conditions which are unseen in the training data, and be trained on significantly less data.\ud \ud Scene invariance is achieved through the use of camera calibration, allowing the system to be trained on one or more viewpoints and then deployed on any number of new cameras for testing without further training. A pre-trained system could then be used as a ‘turn-key’ solution for crowd counting across a wide range of environments, eliminating many of the costly barriers to deployment which currently exist