Intelligent surveillance of indoor environments based on computer vision and 3D point cloud fusion. Issue 21 (30th November 2015)

• Record Type: Journal Article
• Authors: Gómez, María José; García, Fernando; Martín, David; de la Escalera, Arturo; Armingol, José María
• Is Part Of: Expert systems with applications. Volume 42:Issue 21(2015)
• Page Start: 8156
• Page End: 8171
• Publication Date: 2015-11-30
• Subjects: Intelligent Surveillance System -- Sensor fusion -- Computer vision -- 3D point cloud -- Intruder detection; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.06.026
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Surveillance system with or without illumination, for 3D change detection. A multiview-based acquisition method to monitor wide-sized indoor environments. Analysis of the elements detected to identify intruders, new and missed objects. Fusion of colour and 3D features to create an accurate 3D model of the scene. Real-time surveillance algorithm based on the tilt-angle movement. Abstract: A real-time detection algorithm for intelligent surveillance is presented. The system, based on 3D change detection with respect to a complex scene model, allows intruder monitoring and detection of added and missing objects, under different illumination conditions. The proposed system has two independent stages. First, a mapping application provides an accurate 3D wide model of the scene, using a view registration approach. This registration is based on computer vision and 3D point cloud. Fusion of visual features with 3D descriptors is used in order to identify corresponding points in two consecutive views. The matching of these two views is first estimated by a pre-alignment stage, based on the tilt movement of the sensor, later they are accurately aligned by an Iterative Closest Point variant (Levenberg–Marquardt ICP), which performance has been improved by a previous filter based on geometrical assumptions. The second stage provides accurate intruder and object detection by means of a 3D change detection approach, based on Octree volumetric representation, followed by a clusters analysis. The whole scene is continuously scanned, and every captured is compared with the corresponding part of the wide model thanks to the previous analysis of the sensor movement parameters. With this purpose a tilt-axis calibration method has been developed. Tests performed show the reliable performance of the system under real conditions and the improvements provided by each stage independently. Moreover, the main goal of this application has been enhanced, for reliable intruder detection by the tilting of the sensors using its built-in motor to increase the size of the monitored area.