Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm

Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in three-dimensional space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D (Red-Green-Blue and Depth) algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency

Methods to Improve the Field of Intelligent Tutoring Systems using Emotion-based Agents

The aim of this paper is to review select current methods used in the field of Intelligent Tutoring Systems (ITS) with respect to the use of emotion-based agents and how those systems interact with the learner to capture criti-cal data, store the data, and effectively process the data to produce valuable feedback. From this data collected, proposed methods are presented on how to improve existing ITS systems and how to make new ITS’s more effective

Aktuelle Methoden der Background Subtraction und deren Anwendung als Vorverarbeitung einer Gestürzten-Personen-Erkennung

Das Thema dieser Arbeit ist die Entwicklung einer Background Subtraction und deren Verwendung in einer Gestürzten-Personen-Erkennung im Kontext eines Roboter Nachtwächters in einer Pflegeeinrichtung. Dazu wird der aktuelle technische Stand bei der Background Subtraction betrachtet. Im Anschluss daran wird basierend auf der Recherche und den Rahmenbedingungen die durch das Einsatzszenario gegeben sind ein Ansatz gewählt und umgesetzt.The topic of this thesis is the development of a background subtraction and its use in a fallen person detection in the context of a robot night watchman in a care facility. For this purpose, the current technical status of background subtraction is considered. Subsequently, an approach is selected and implemented based on the research and the conditions given by the application scenario

Moving Object Detection based on RGBD Information

This thesis is targeting the Moving Object Detection topic, more specifically, the Background Subtraction. In this study, we proposed two approaches using color and depth information to solve the background subtraction. The following two paragraphs will give a brief abstract for each approach. In this research study, we propose a framework for improving traditional Background Subtraction techniques. This framework is based on two data types: color and depth; it stands for obtaining preliminary results of the background segmentation using Depth and RGB channels independently, then using an algorithm to fuse them to create the final results. The experiments on the SBM-RGBD dataset using four methods: ViBe, LOBSTER, SuBSENSE, and PAWCS, proved that the proposed framework achieves an impressive performance compared to the original RGB-based techniques from the state-of-the-art. This dissertation also proposes a novel deep learning model called Deep Multi-Scale Network (DMSN) for Background Subtraction. This convolutional neural network is built to use RGB color channels and Depth maps as inputs with which it can fuse semantic and spatial information. Compared with previous Deep Learning Background Subtraction techniques that lack information due to their use of only RGB channels, our RGBD version can overcome most of the drawbacks, especially in some particular challenges. Further, this study introduces a new protocol for the SBM-RGBD dataset regarding scene-independent evaluation, dedicated to Deep Learning methods to set up a competitive platform that includes more challenging situations. The proposed method proved its efficiency in solving the background subtraction in complex problems at different levels. The experimental results verify that the proposed work outperforms the state-of-the-art on SBM-RGBD and GSM datasets

An Improved colorimetric invariants and RGB-Depth-based Codebook model for background subtraction using kinect

3th Mexican International Conference on Artificial Intelligence, Chiapas, MEXIQUE, 16-/11/2014 - 22/11/2014In this paper we propose to join the benefits of multiple in- variant information into the well-know background subtraction method 'Codebook'. Indeed, this method mainly repose on a color model allowing a separate process of color and intensity distortion. In order to manage hard situations involving high illumination changes, we propose to enhance this model with the use of two supplementary steps: 1/ transforming the input color image using a colorimetric invariant in order to obtain a color-invariant image whatever the illumination conditions; 2/ using depth information as a new data inside the Codebook model, thus performing an RGB-D fusion during the segmentation process