# Dynamic Queries through Augmented Reality for Intelligent Video Systems
## 1 Introduction  
A distributed video surveillance system consists of interconnected cameras that share information in specific environments for monitoring the human activities, motion detection, behavior, and interaction between individuals through the tracking of moving objects [1, 2, 3, 4], thus providing great versatility in obtaining information for analyzing the acquired data. One of the main purposes of the distributed camera system is to provide an automatic interpretation of the scenes and predict the interactions of the objects; however, there are different factors that can provide an automatic understanding of the scenes and predict the interactions of the objects. Some of the different factors to consider when acquiring information about a specific scenario are the characteristics of the cameras available, the position, and the number of cameras [5, 6]. One of the significant factors to consider wheninstalling a video surveillance system is that, when increasing the number of cameras, the coverage area increases, and so does the amount of information to manipulate. Every day, we are exposed to different types of surveillance carried out with devices that constantly generate video data [2,7]. According to the analyses carried out by [8,9], the increase in information obtained by video surveillance devices has grown exponentially to the point that up to 100 GB in data is acquired for each video surveillance camera in a monthly period. Particularly, IntelliVision have deployed more than four million surveillance cameras [10]. In video surveillance systems, various challenges must be met to achieve the interconnection and exchange in information from one point to another through different functional units such as cameras, means of sending information, and storage units. With an increase in distributed systems, it is necessary to propose or apply architectures that help to achieve interoperability between the different existing elements, such as sensors and actuators, to facilitate the sending and reception of data. Some works, such as [11, 12, 13, 14, 15], describe different middleware architectures to carry out the exchange in data and use of information in addition to describing the elements that influence successful interoperability between different sources of information, such as the communication medium, programming techniques, software architecture, sensors, semantics, and adaptive control, among others. The most common use of the information obtained from a distributed camera system is motion detection and the tracking of objects of interest [3]. Distributed camera systems have proven useful as a passive means of detecting events or situations of interest; for example, the detection of an atypical behavior [16, 17, 18], the evolution of the dynamics of movement of an object in time [19, 20, 21], target tracking for surveillance applications [3, 22, 23, 24, 25, 26, 27], and tridimensional reconstruction [28, 29, 30]. Other important works are [16, 31, 32, 33, 34, 35]. These works have already shown that they are useful for recognizing events of interest in a defined context. However, it has been observed that the complexity of these approaches increases considerably when the local dynamics are very complicated [36] or when there is a distributed system of cameras [37]. Many authors have proposed different methodologies and tools to automatically or semi-automatically detect atypical events in a monitored area. Currently, the high quality and high rate of image acquisitions per second are more frequently used in video surveillance systems, for which increasingly innovative methods are being presented to find motion primitives that help to interpret the activities raised in the stage [38]. Due to the diversity of approaches that exist, the fusion of information is a complex task due to the parameters each one requires and the results each delivers. Table 1 shows a table that represents the heterogeneity in the results when applying different object-tracking methods [39].