Abandoned Object Detection using Frame Differencing and Background Subtraction

In wireless sensor networks, it is crucial to support the collected data of sensor nodes with position information. One of the promising ways to acquire the position of unknown nodes is using a mobile anchor node that traverses throughout the network, stops at determined points, and sends its position to aid in obtaining the location of other unknown nodes. The main challenge in using mobile anchor nodes lies in designing the path model with the highest localization accuracy, shortest path length, full coverage area, and minimal power consumption. In this paper, a path model named the Arrow-Curve path model is proposed for mobile node aided localization. The proposed path model can effectively localize all the static unknown sensor positions in the network field with high positioning accuracy and low power consumption while pledging full coverage area. The sensor network is implemented using MATLAB simulation and MCU node in both static unknown nodes and the mobile anchor node. The realtime environment guarantees a realistic environmental model with reliable results. The path model is implemented in realtime in indoor and outdoor environments and compared to the H-Curve path model using a trilateration technique. The results show that the suggested path model achieves better results compared to H-Curve model. The proposed path model achieves an average position error less than that of H-Curve by 10.6% in a simulation environment, 5% in an outdoor realtime environment, and 9% in an indoor realtime environment, and it decreases power consumption by 62.65% in the simulation environment, 50% in the outdoor realtime environment, and 75% in the realtime environment in indoor compared to H-Curve.

A numerical simulation tool for predicting the impact of outdoor thermal environment on building energy performance

Providing high data rates and wide bandwidth applications are the most important challenges that mobile systems and radio networks are trying to accomplish. Radio over Fiber-RoF systems is a promoting research field since it benefits of Passive Optical Network (PON) advantages and unlicensed large bands in 60 GHz millimeter band (57-64GHz). In this study, we consider a complete RoF system from the central office to the end-user passing by the base station and we study the impact of propagation environment on the whole system performance and radio coverage by considering different scenarios for multipath propagation (Line of Sight-LOS, Non Line of Sight-NLOF). In particular, we have been interested in Rayleigh and Rice propagation models for both indoor and outdoor environments. Optisystem/ADS Co-simulation has been investigated to simulate RoF network coverage for indoor and outdoor environments while considering high data rates.

Robust real-time eye detection and tracking under variable lighting conditions and various face orientations

In computer vision application object tracking is a challenging problem. Illumination and occlusion are major constraints observed in object tracking. We are focusing on object tracking with partial or full occlusion. Object tracking is done using features like colors and contours. We have proposed a robust Color-based algorithm to track the object and handle occlusion in real time domain. In our algorithm, HSV color model is used and HSV range of the color object to be tracked is selected. Required object area is identified and contour is formed accordingly to track the object. Proposed algorithm is implemented on ARM Cortex-A7 using Open Source Computer Vision (OpenCV) and Linux-embedded platform. The experiment is done using generated real life database and standard unusual crowd activity database. Results showed run time performance, efficiency and accuracy in real time environment.

Fault tolerant Resource consumption in Desktop grid is a motivational area in research. The present research paper focuses on the dimensions of Fault Tolerant resource usage especially in area of available computational power. Desktop grid resources are accountable for generation of computational power. Alchemi Desktop middleware is useful for collection of computational power on diverse machines in Microsoft window based environment. Failure and Fault in execution side can create serious problem, in addition to a direct impact on computational power in Real Time Environment. In the Environment of faults, control on the available computational power is very necessary in grid middleware. This problem has not been addressed so far. Alchemi Desktop Grid Middle ware provide manual Procedure for control of computational power in Real Time Environment. There is no automated mechanism available for controlling the processing power in alchemi desktop grid. This Research work has proposed, designed & developed automated framework for Alchemi Grid middleware. Framework can take control on available computational power in Real Time Environment at Time of Fault in execution processes. Testing for the framework is done in Real Time environment. Results after test show that framework gives quick response for controlling available computational power. Framework is able to detect defective process machine and correct fault in milliseconds which will cooperative to maintain level of available computational power In Real time Environment. This Research work has tried to eliminate Manual Procedure for controlling computational power by using automated Method for quick action in case of execution side faults.

This paper proposes a method of tracking an object for a person-following mobile robot by combining a monocular camera and a laser scanner, where each sensor can supplement the weaknesses of the other sensor. For human-robot interaction, a mobile robot needs to maintain a distance between a moving person and itself. Maintaining distance consists of two parts: object tracking and person-following. Object tracking consists of particle filtering and online learning using shape features which are extracted from an image. A monocular camera easily fails to track a person due to a narrow field-of-view and influence of illumination changes, and has therefore been used together with a laser scanner. After constructing the geometric relation between the differently oriented sensors, the proposed method demonstrates its robustness in tracking and following a person with a success rate of 94.7% in indoor environments with varying lighting conditions and even when a moving object is located between the robot and the person.

Tracking objects or humans using background subtraction is commonly used both in indoor and outdoor environment. But this method may not produce quality results due to illumination change, shadows, and occlusion occurred in indoor or outdoor environments. Song and Nevatia suggested a simultaneous tracking of face and body for human tracking in an indoor environment using Adaboost face detection and Camshift color tracking methods (2004). Changes in illumination are not considered in their work. In this research, an intelligent method for color tracking scheme using skin reflectance values is proposed to track the human in real-time video for varying illuminations both in indoor and outdoor environments. This research finds application in the live transmission of an indoor video conference or a live news cast from an outdoor as well as in the places where the illumination change occurs frequently

We demonstrate that structured light-based depth sensing with standard perception algorithms can enable mobile peer-to-peer interaction between humans and robots. We posit that the use of recent emerging devices for depth-based imaging can enable robot perception of non-verbal cues in human movement in the face of lighting and minor terrain variations. Toward this end, we have developed an integrated robotic system capable of person following and responding to verbal and non-verbal commands under varying lighting conditions and uneven terrain. The feasibility of our system for peer-to-peer HRI is demonstrated through two trials in indoor and outdoor environments.

Language acquisition by children depends on the learning environment when they were in kindergarten to some extent. The goals of this study were to find out the impact of the indoor and outdoor environment on children's language development. Structural equation modeling was adopted. Children randomly selected from 270 preschool classes of five provinces in China were recruited in this study (1642 children aged between 36 and 72 months). We concluded that language achievement of children cannot be directly predicted by indoor and outdoor facilities and play materials, and an intermediate transmission pathway was needed. As an intermediary variable, the curriculum implementation partially mediated the influence of the indoor learning center on children's language achievement. The psychological atmosphere and curriculum implementation have a chain mediation effect between indoor and outdoor facilities and play material quality and childhood language achievement, and the influence effect of indoors was different from that of outdoors. In terms of the influence of indoor learning center planning and play materials, the effect on children's language achievement accounted for 61.5%, while when it came to that of outdoor learning center facilities and equipment, it was 60.7%. This indicated that the impact of the indoor and outdoor environment on children's language development could not be ignored. The indoor environment was more dependent than the outdoor environment on the chain role played by class curriculum implementation, psychological atmosphere and curriculum implementation quality. Therefore, teachers should create a positive psychological atmosphere, organically combine the curriculum implementation with environmental planning and material delivery, cultivate a prepared language environment for children and support their active learning and development.

Omnidirectional catadioptric cameras offer a large field of view and a complete information about the world surrounding the robot. In this paper, we describe a method to perform a fast and robust extraction of the omnidirectional free space using active contour models. The extracted free space could help the robot in real time navigation and environment exploration tasks. The presented approach will be compared to classical active contour methods usually applied to object segmentation. Some comparative results achieved in indoor and outdoor environments are shown to validate the approach.

In recent years, Channel State Information (CSI)-based Human activity recognition (HAR) has received extensive attention for its low cost and privacy protection property. Existing CSI-based HAR systems utilize single classifier to accomplish recognition, which results in poor robustness and relatively low recognition accuracy. In this paper, a random subspace classifier ensemble method is proposed for classification, which utilizes the frequency domain feature instead of the time domain feature and each kind of feature is selected in the same amount. We evaluate its performance both in typical indoor and outdoor environment. Our results show, the recognition accuracy can reach 91.2% and 90.2% in outdoor and indoor environment, respectively.

We present an efficient vision-based global topological localization approach in which different image features are used in a coarse-to-fine matching framework. Orientation Adjacency Coherence Histogram (OACH), a novel image feature, is proposed to improve the coarse localization. The coarse localization results are taken as inputs for the fine localization which is carried out by matching Harris-Laplace interest points characterized by the SIFT descriptor. The computation of OACHs and interest points is efficient due to the fact that these features are computed in an integrated process. The matching of local features is improved by using approximate nearest neighbor searching technique. We have implemented and tested the localization system in real environments. The experimental results demonstrate that our approach is efficient and reliable in both indoor and outdoor environments. This work has also been compared with previous works. The comparison results show that our approach has better performance with higher correct ratio and lower computational complexity.

The objective of this study is to characterize indoor and outdoor levels of volatile organic compounds (VOCs) and formaldehyde (HCHO) and identify indoor emission sources in thirty elderly care centers (ECCs) located in the Seoul metropolitan city and Gyeonggi province in Korea. Air monitoring samples from indoor and outdoor environments were collected from January to December in 2007. Statistical analyses of indoor and outdoor VOCs and HCHO levels in three rooms (a bedroom, living, and dining rooms) of each ECC were performed, and these were compared to identify environmental factors associated with an increase of indoor pollution levels. Total volatile organic compounds (TVOC) levels were significantly (p<0.05) different between indoor (230.7±1.7 μg/m3) and outdoor (137.8±1.9 μg/m3) environments, with an I/O ratio of 1.67. The indoor HCHO level (20.1±1.6 μg/m3) was significantly (p<0.05) higher than the outdoor level (8.1±1.9 μg/m3), with an I/O ratio of 2.48. Indoor VOCs and HCHO levels in the bedrooms were significantly (p<0.05) higher than those in the living and dining rooms. Furthermore, indoor levels of VOCs and HCHO at ECCs were significantly (p<0.05) different depending on environmental factors such as the use of carpet, paint, and wooden furniture. In multiple regression analysis, indoor VOCs and HCHO levels at ECCs were significantly (p<0.05) correlated with two micro-environmental factors: the use of carpet and paint. This study confirmed that indoor VOCs and HCHO levels were significantly higher than those in outdoor environments. These air pollutants were mainly emitted from indoor sources, such as carpet, paint, and construction materials at the ECCs in Korea.

The traffic sign detection, recognition of signs is a vital part of automatic traffic sign identification system. Its main objective is to guide the driver to be aware of the essential traffic information during driving on the road and to be a major part of automated on-road driving system. The aim of the proposed work is to detect traffic sign which has illumination variation, partial occlusion and to classify using less computational resources and with reduced time complexity in real time environment. Motion model is designed to perform prediction for estimating the traffic sign with the help of kalman filter. Appearance model in the form of adaptive training based detector is used for the final traffic sign detection.