261 research outputs found
Development of Dynamic Background Subtraction Algorithms for Visual Surveillance Applications
No full textRecent developments in the field of computer vision and the easy availability of low-cost digital cameras have spurred demands for video surveillance systems. These advancements have helped manufacturers to market surveillance cameras as a consumer electronic product for both personal and public use. Surveillance cameras have a wide range of applications such as security, anomaly detection, traffic control, congestion analysis, target tracking, and daycare/ nanny monitoring.
Background subtraction (BS) is a fundamental step for moving object detection in various video surveillance applications. It detects a moving object by comparing every incoming frame with the up-to-date (learned) statistical background model. Based on the deviation from the background model, it classifies pixels into foreground or background pixels. Dynamic environments such as swaying trees, ripples in water, fast and gradual changes in illumination, relocation of background objects, initialization with moving objects, and crowded scenes make background modelling a challenging task. Moreover, problems like shadows, camera jitters, and noise make it even more difficult. Therefore, it is important to develop efficient BS algorithms that can address these challenges and offer effective performance in real-time scenarios.
In this thesis, robust BS techniques are developed to handle dynamic background conditions. Initially, algorithms relating to background modelling are proposed. Here, new features which are robust to dynamic backgrounds are developed using Gabor filter and the concept of difference domain. In the later part of the thesis, new foreground detection algorithms are formulated using Wronskian change detection model (WM).
A novel fuzzy colour difference histogram (FCDH) based BS algorithm has been proposed by employing fuzzy c-means (FCM) clustering and colour difference histogram (CDH). This is done by measuring the colour difference between a pixel and its neighbourhood. The use of CDH reduces the number of false errors due to the non-stationary background, illumination variation, and camouflage. The use of the FCM clustering algorithm in CDH reduces the large dimensionality of the histogram bins in the computation.
The length of the feature vector in FCDH based BS algorithm depends on the number of clusters used in the FCM. If the number of clusters taken is large, then the length of the feature vector becomes large. Moreover, FCDH does not completely remove the effect of dynamic background. In order to overcome the limitations posed by the FCDH based BS, a multi-modal BS algorithm based on difference of intensity between the original and the quantised image is computed. Further, the difference of intensity values between the original and the Gaussian filtered image is also calculated. To reduce the effect of noise and small variations, the difference of the original intensity values with the local mean of the reduced quantised value is performed. Further, to reduce the effect of a dynamic background such as swaying trees, spouting fountains and flowing rivers, the difference of original intensity values with the local mean of the Gaussian filtered image is determined. The effect of cluttered environment is reduced due to the blurring introduced by the Gaussian filtered image.
The use of Gabor feature in BS is the next contribution. A hierarchical BS algorithm using Gabor filter is proposed where both the block-based and the pixel-based approaches are combined. Both coarse and fine level background modelling is done using the magnitude feature obtained from the Gabor filter. First, the coarse level background modelling is accomplished for identifying blocks which are fully or partially occupied by the foreground objects. In the second stage of pixel-level analysis, a pixel in a foreground block is further analysed and then classified using the Gabor feature for improving the precision of the detected moving object. The number of features extracted from the Gabor filter for the hierarchical BS is large. In order to reduce the length of the feature vector generated from the Gabor filter, a novel Gabor Hu moment is proposed from the orientations of the Gabor filter at different scales and is used as a feature in the codebook BS algorithm for efficient moving object detection. The use of Gabor features makes the algorithm less susceptible to background variations and models the background in an efficient manner for accurate silhouette detection of the foreground object.
Foreground detection algorithms are proposed using the Wronskian change detection model (WM). An adaptive spatio-temporal BS technique using improved WM in Gaussian mixture model (GMM) framework is developed for moving object detection. The GMM does not support the spatial relationship among neighbouring pixels and uses a fixed learning rate for every pixel during the parameter update. On the other hand, WM is a spatial-domain BS technique which solves the misclassification of pixels but fails in the presence of a dynamic background. To address this, a novel spatio-temporal BS technique is proposed, which exploits spatial relation of Wronskian function and employs it with a new fuzzy adaptive learning rate in GMM framework. Instead of using WM directly, an improved WM (IWM) is proposed by adaptively finding out the ratio of the current pixel to the background pixel or its reciprocal. Further, a weighted Wronskian function is developed to mitigate the effect of dynamic background pixels. A new multi-channel and multi-resolution Wronskian change detection model (MCMRWM) based codebook BS algorithm is proposed for moving object detection in the presence of dynamic background conditions. In the proposed MCMRWM, the multi-channel information helps to reduce the false negative of the foreground object; and the multi-resolution data suppresses the background noise resulting in reduced false positives. The proposed algorithm considers the ratio between feature vectors of current frame and the background model or its reciprocal in an adaptive manner, depending on the l2 norm of the feature vector, which helps to detect the foreground object completely without any false negatives.
From experimental results, it is observed that the proposed algorithms exhibit a considerable improvement in moving object detection and hence are expected to be quite useful in applications like traffic monitoring, target tracking, security, and surveillance
Modelling and Optimization of Multiple Process Attributes of Electrodischarge Machining Process by Using a New Hybrid Approach of Neuro–Grey Modeling
No full textMotion detection, object classification and tracking for visual surveillance application
No full textVisual surveillance in dynamic scenes, especially for humans and vehicles, is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism, crime, public safety and for efficient management of traffic. The work involves designing of efficient visual surveillance system in complex environments. In video surveillance, detection of moving objects from a video is important for object classification, target tracking, activity recognition, and behavior understanding. Detection of moving objects in video streams is the first relevant step of information and background subtraction is a very popular approach for foreground segmentation. In this thesis, we have simulated different background subtraction methods to overcome the problem of illumination variation, background clutter, shadows, and camouflage. Object classification is done using silhouette template based classification to categorize objects into human, group of human and vehicle. Detecting and tracking of human body parts is important in understanding human activities. We have proposed two methods to overcome the problem of object tracking in varying illumination condition and background clutter. For target tracking of interested object in the consecutive video frames, we have used normalized correlation coefficient (NCC). NCC is robust to varying illumination condition. Template is updated on every frame to minimize the template drift problem and it also tries to cope with short-lived occlusion and background clutter. In order to extend the surveillance area and overcome occlusion, fusion of data from multiple cameras is employed in our project. We have tracked objects across multiple cameras with non-overlapping FOVs based on object appearances. A brightness transfer function (BTF) is determined from the cumulative histograms of the images. Matching of the object is done, with the help of Bhattacharya distance
Regression Analysis of Grid Stability under Decentralized Control
No full textThis is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordDecentralized smart grid control (DSGC) aims to implement smart control strategies through the demand response without significant reinforcements in the grid infrastructure. This strategy aims to balance the demand and supply considering the dynamic and economic price structure of the grid. Demand response strategy is implemented based on the heterogeneous nature of response of the consumers to the electricity price. In this paper, feature selection and regression analysis have been performed to study the dependence of the stability condition and system parameters on the parameters of decentralized control like the reaction time of the consumer, power produced or consumed and the price elasticity. The analysis is performed based on the data generated considering 10,000 Monte Carlo simulations of the initial conditions, which incorporates different characteristics of the heterogeneous consumers
Reliability Evaluation and Analysis of Mobile Ad Hoc Networks
No full textThe paper addresses the reliability problem of mobile ad hoc networks under link and node failure model. Node reliability is calculated as a function of no. of neighbor nodes, packet success rate, and device type and packet size. The presence of a link between any node pair is binary and its reliability is computed considering the distance between nodes and signal-to-noise ratio (SNR). An efficient algorithm is proposed to analyze and calculate the reliability of mobile ad hoc networks considering multiple routes from source and destination nodes. The effect of different parameters on node reliability and link reliability are analyzed and discussed. The network is simulated and analyzed using INET frame work. Reliability of two distinct cases of this simulation is evaluated. The simulated results and discussions ensure that evaluation of the reliability of any mobile ad hoc networks can be done easily and in an efficient manner by the proposed method
A high-efficiency CIGS solar cell design employing BaSnO₃ as ETL and CsPbI₃ as HTL for improved stability and charge transport
No full textABSTRACT: The selection of the electron transport layer (ETL) and hole transport layer (HTL) has a significant impact on the performance of CIGS (Copper Indium Gallium Selenide) solar cells. In this work, we use SCAPS-1D simulation to examine how different ETL and HTL materials affect the stability and efficiency of solar cells based on CIGS. Because of its wide bandgap, high electron mobility, and superior thermal and chemical stability, BaSnO₃ emerges as a promising alternative to the conventional designs that usually use CdS and ZnO as ETL materials. Utilising its exceptional optoelectronic characteristics, high hole mobility, and chemical robustness, CsPbI₃ is presented for the HTL. BaSnO₃ and CsPbI₃ combine to create a novel device architecture that improves photovoltaic performance and environmental compatibility. Notable power conversion efficiency of 16.41 %, open-circuit voltage of 1.1244 V, short-circuit current density of 19.76 mA/cm2, and fill factor of 73.86 % are all displayed by the simulated solar cell. These findings demonstrate how crucial careful material selection and device architecture optimisation are to improving thin-film photovoltaic technologies' stability and efficiency. The suggested structure offers a convincing route to sustainable, next-generation solar energy solutions
Dynamic background subtraction using Local Binary Pattern and Histogram of oriented Gradients
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