An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system

Abstract

Please cite this article as: Y. Hu, et al., An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system, Robotics and Autonomous Systems (2016), http://dx.doi.org/10. 1016/j.robot.2016.08.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system AbstractIn this paper, we design a fast background subtraction algorithm and deploy this algorithm on a monitoring system based on NAO humanoid robot. The proposed algorithm detects a contiguous foreground via a contiguously weighted linear regression (CWLR) model. It consists of a background model and a foreground model. The background model is a regression based low rank model. It seeks a low rank background subspace and represents the background as the linear combination of the basis spanning the subspace. The foreground model promotes the contiguity in the foreground detection. It encourages the foreground to be detected as whole regions rather than separated pixels. We formulate the background and foreground model into a contiguously weighted linear regression problem. This problem can be solved efficiently via an alternating optimization approach which includes continuous and discrete variables. Given an image sequence, we use the first few frames to incrementally initialize the background subspace, and we determine the background and foreground in the following frames in an online scheme using the proposed CWLR model, with the background subspace continuously updated using the detected background information. The proposed algorithm is implemented by Python on a NAO humanoid robot based monitoring system. This system consists of a control station and a Nao robot. The Nao robot acts as a mobile probe. It captures image sequence and sends it to the control station. The control station serves as a control terminal. It sends commands to control the behaviour of Nao robot, and it processes the image data sent by Nao. This system can be used for living environment monitoring and form the basis for many vision-based applications like fall detection and scene understanding. The experimental comparisons with most recent algorithms on both benchmark dataset and NAO captures demonstrate the high effectiveness of the proposed algorithm.

Implementación de un modelo de reconocimiento visual para mejorar el modelo adaptativo en niños con daltonismo usando un robot Nao para niños vulnerables en una ciudad inteligente

“…Monocromática o acromatopsia, es la condición más rara de daltonismo, en este, todos los conos están ausentes, por lo tanto, solo el ambiente se percibe en escalas de grises o brillo y, aunque es muy raro, representa una gran dificultad para las personas que la padecen, ya que no pueden llevar una vida normal sin la asistencia de personas sanas. Estas personas no pueden beber un líquido de una botella sin antes buscar una etiqueta que confirma su contenido, no pueden identificar si un alimento está en buenas condiciones antes de comerlo, pueden no elegir su ropa, identificar un espacio de estacionamiento, entre otras dificultades [5]. Aproximadamente el 10% de las personas sufre de alguna deficiencia de color o ceguera, es decir, alrededor de 700 millones de personas sufren del daltonismo, teniendo en cuenta que la población mundial supera los 7.000 millones de habitantes.…”

Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

“…Moreover, Hu et al ( 2016) implemented a monitoring system for NAO humanoid robot using the design of a fast background subtraction algorithm. The suggested approach tries to convert the background and foreground models into a problem of contiguous weighted linear regression [92]. The proposed monitoring system can monitor the living environment and serve as the foundation for various vision-based applications, such as fall detection.…”

Threshold adaptation and XOR accumulation algorithm for objects detection

“…Background subtraction is a necessary task in video applications such as surveillance to track, index, retrieve, and capture the essential metadata of people, cars, and other different moving objects either in real-time or off-time [12]- [16]. It is a starting point for higher processing tasks of video systems such as object identification, detection, and tracking over video sequences in recent applications and researches [17]- [21]. It is stated that many background models require the adjustment of initial frames to upgrade the background without any movement over time.…”