A System for the Detection of Persons in Intelligent Buildings Using Camera Systems—A Comparative Study

Abstract

This article deals with the design and implementation of a prototype of an efficient Low-Cost, Low-Power, Low Complexity–hereinafter (L-CPC) an image recognition system for person detection. The developed and presented methods for processing, analyzing and recognition are designed exactly for inbuilt devices (e.g., motion sensor, identification of property and other specific applications), which will comply with the requirements of intelligent building technologies. The paper describes detection methods using a static background, where, during the search for people, the background image field being compared does not change, and a dynamic background, where the background image field is continually adjusted or complemented by objects merging into the background. The results are compared with the output of the Horn-Schunck algorithm applied using the principle of optical flow. The possible objects detected are subsequently stored and evaluated in the actual algorithm described. The detection results, using the change detection methods, are then evaluated using the Saaty method in order to determine the most successful configuration of the entire detection system. Each of the configurations used was also tested on a video sequence divided into a total of 12 story sections, in which the normal activities of people inside the intelligent building were simulated.

User Experience Sensor for Man–Machine Interaction Modeled as an Analogy to the Tower of Hanoi

“…The following aspects and approaches prove to be helpful: Active Beamforming [ 24 ]—gives the possibility to filter the acquired sound basing on the source location in two- or three-dimensional space, using a microphone matrix. Using Active Beamforming within the vision system [ 25 ] feedback loop [ 26 ] provides additional information on the location of the interlocutor, and the beamforming subsystem can be reconfigured to target the acquisition coordinates to a specific area/direction, Speech-to-text conversion—a spectrogram of a specified time segment of the recorded sound sample can be analyzed based on large recursive neural networks [ 27 , 28 ]. In order to improve the recognition efficiency of the most frequently issued commands, the system has been expanded with an additional context containing quantitative frequency data generated by FFT for each of the system users and a subsystem of enhancing important signal features [ 29 ], Natural language processing in the above-described system is designed to convert text into structured data and determine the interlocutor’s intentions along with the preparation of data for the expert network, Expert Bayesian Networks [ 30 ]—for making decisions in the conditions of incompleteness and uncertainty of input data based on the structured context provided by NLP as well as subsystems providing only context data (video subsystem, quality subsystem).…”

“…Active Beamforming [ 24 ]—gives the possibility to filter the acquired sound basing on the source location in two- or three-dimensional space, using a microphone matrix. Using Active Beamforming within the vision system [ 25 ] feedback loop [ 26 ] provides additional information on the location of the interlocutor, and the beamforming subsystem can be reconfigured to target the acquisition coordinates to a specific area/direction,…”

Center of Gravity Coordinates Estimation Based on an Overall Brightness Average Determined from the 3D Vision System

“…This system makes it possible to decide on a sensing object position in a working envelope followed by the parameters of the center of gravity (x, y, and z), as one of the goals in this paper. Such obtained information follows the control system of the robotic arm for their successive transposition [28]. The source point for the introduced solution consists of the realized technical solution with the object coordinates measuring to the object to determine its center of gravity in 3D space.…”

Means of IoT and Fuzzy Cognitive Maps in Reactive Navigation of Ubiquitous Robots

“…Internet of Things (IoT) has found many applications in various areas since its breakthrough to praxis about 10 years ago. For instance, we can find it as an assistant for many human activities in the frame of smart homes [1,2], healthcare, smart cities [3], smart agriculture [4], transportation, or manufacturing. IoT is also an important element in such concepts as cyber-physical systems [5] and Industry 4.0 [6].…”