Abstract
Real-time and accurate background modeling is an important researching topic in the fields of remote monitoring and video surveillance. Meanwhile, effective foreground detection is a preliminary requirement and decision-making basis for sustainable energy management, especially in smart meters. The environment monitoring results provide a decision-making basis for energy-saving strategies. For real-time moving object detection in video, this paper applies a parallel computing technology to develop a feedback foreground–background segmentation method and a parallel connected component labeling (PCCL) algorithm. In the background modeling method, pixel-wise color histograms in graphics processing unit (GPU) memory is generated from sequential images. If a pixel color in the current image does not locate around the peaks of its histogram, it is segmented as a foreground pixel. From the foreground segmentation results, a PCCL algorithm is proposed to cluster the foreground pixels into several groups in order to distinguish separate blobs. Because the noisy spot and sparkle in the foreground segmentation results always contain a small quantity of pixels, the small blobs are removed as noise in order to refine the segmentation results. The proposed GPU-based image processing algorithms are implemented using the compute unified device architecture (CUDA) toolkit. The testing results show a significant enhancement in both speed and accuracy.
Highlights
In the sustainable energy-saving strategies, smart meters provide an effective power reduction support by controlling electricity with intelligent functions [1]
To further improve the processing speed, this paper proposes to utilize a graphics processing unit (GPU) programming method instead of CPU to speed up the feedback background modeling, foreground segmentation, and connected component labeling (CCL) algorithms
For CCL acceleration, we develop a novel parallel connected component labeling (PCCL) algorithm to speed up the image processing of the dependent situation
Summary
In the sustainable energy-saving strategies, smart meters provide an effective power reduction support by controlling electricity with intelligent functions [1]. The monitoring module aims to sense the environmental information, including temperature, humidity, foreground objects, and other datasets These sensing datasets are converted into several condition variables for evaluating an environment situation, which are transmitted to smart meters via a low-cost and wireless sensor network based on ZigBee communication protocol [2]. The noisy spot is a blob of a small quantity of pixels in the segmented binary foreground result Based on this definition, it is an effective solution to count pixels in each blob and remove the small blobs as noise. To further improve the processing speed, this paper proposes to utilize a graphics processing unit (GPU) programming method instead of CPU to speed up the feedback background modeling, foreground segmentation, and CCL algorithms. For CCL acceleration, we develop a novel parallel connected component labeling (PCCL) algorithm to speed up the image processing of the dependent situation.
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