Context-aware intelligent video analysis
for the management of smart buildings
Title: Context-aware intelligent video analysis for the management of smart buildings
Keywords: Smart camera, artificial intelligence (AI), building information modeling (BIM), ontologyengineering, real-time knowledge fusion, people detection, people tracking, event detections, multi-cameramulti-space dataset.Abstract:To date, computer vision systems are limited toextract digital data of what the cameras “see”.However, the meaning of what they observe could begreatly enhanced by environment and human-skillsknowledge.In this work, we propose a new approach to cross-fertilize computer vision with contextual information,based on semantic modelization defined by anexpert.This approach extracts the knowledge from imagesand uses it to perform real-time reasoning accordingto the contextual information, events of interest andlogic rules. The reasoning with image knowledgeallows to overcome some problems of compute
vision such as occlusion and missed detections and to offer services such as people guidance and people counting.
The proposed approach is the first step to develop an “all-seeing” smart building
that can automatically react according to its evolving information, i.e., a context-aware smart building.
The proposed framework, named WiseNET, is an artificial intelligence (AI) that is in charge of taking decisions in a smart building (which can be extended to a group of buildings or even a smart city).
This AI enables the communication between the building itself and its users to be achieved by using a language understandable by humans.
