GPU based extraction of moving objects without shadows under intensity changes

Fukui, Shinji; Iwahori, Yuji; Woodham, Robert J.

doi:10.1109/cec.2008.4631366

Cited by 11 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multicore CPU and graphic processing unit (GPU) have recently used to achieve the real-time performance of computer vision [3] [10] [13]. However, as the consideration of performance, they are designed for different types of task.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Object Detection for Multi-Camera on Heterogeneous Parallel Processing Systems

Lin

Teng

Chen

et al. 2013

2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems

View full text Add to dashboard Cite

In recent years, the need for object detection has significantly increased for multi-camera systems. However, the detection methods in such systems incur high computational cost, which leads to a major challenge in real-time applications. In this work, we propose a Scissor Algorithm for object detection using a multi-core CPU and a graphic processing unit (GPU). Leveraging the features of both the CPU and the GPU, the object detection method was enhanced in two stages: (a) pixel-to-pixel color filtering and (b) grouping. The proposed algorithm can effectively shrink the search area for detection and further improve the process of detection, thus effectively increasing the frame rate for real-time applications. Experimental results demonstrate the real-time performance of the proposed algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Object Detection for Multi-Camera on Heterogeneous Parallel Processing Systems

Lin

Teng

Chen

et al. 2013

2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems

View full text Add to dashboard Cite

show abstract

“…Improved programmability, together with the low price of GPUs has resulted in the current trend of general purpose computing on a GPU (GPGPU). Fukui et al reported a speedup in background subtraction on the CIELAB color space using a GPU [16]. Griesser et al also reported a speedup in the background segmentation algorithm based on the extended collinearity criterion using a GPU [17].…”

Section: Introductionmentioning

confidence: 99%

“…However, GPGPU has attracted much attention lately in the research field [16], [17] as a result of recent development in GPUs. In current GPUs, programmable pipelines support vectorized floating-point operations, which enable fast processing.…”

Section: Advantage and Disadvantage Of Gpu Programmingmentioning

confidence: 99%

Real-Time Object Detection Using Adaptive Background Model and Margined Sign Correlation

Yamamoto

Iwai

Ishiguro

2011

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Ayaka YAMAMOTO†a) , Nonmember, Yoshio IWAI †b) , and Hiroshi ISHIGURO †c) , Members SUMMARYBackground subtraction is widely used in detecting moving objects; however, changing illumination conditions, color similarity, and real-time performance remain important problems. In this paper, we introduce a sequential method for adaptively estimating background components using Kalman filters, and a novel method for detecting objects using margined sign correlation (MSC). By applying MSC to our adaptive background model, the proposed system can perform object detection robustly and accurately. The proposed method is suitable for implementation on a graphics processing unit (GPU) and as such, the system realizes real-time performance efficiently. Experimental results demonstrate the performance of the proposed system.

show abstract

“…They created a pixel-wise probability distribution of intensity, which was updated by each allocated pixel thread in parallel. To remove shadow effect under intensity changes, Fukui et al [27] recorded illumination intensity and RGB (red, green, blue) color information into a histogram table as an adaptive background model. The intensity and color threshold of each pixel were generated from their distribution stored in the corresponding histogram.…”

Section: Related Workmentioning

confidence: 99%

GPU-Accelerated Foreground Segmentation and Labeling for Real-Time Video Surveillance

Song

Tian

Fong³

et al. 2016

Sustainability

View full text Add to dashboard Cite

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.

show abstract

GPU based extraction of moving objects without shadows under intensity changes

Cited by 11 publications

References 9 publications

Real-Time Object Detection for Multi-Camera on Heterogeneous Parallel Processing Systems

Real-Time Object Detection for Multi-Camera on Heterogeneous Parallel Processing Systems

Real-Time Object Detection Using Adaptive Background Model and Margined Sign Correlation

GPU-Accelerated Foreground Segmentation and Labeling for Real-Time Video Surveillance

Contact Info

Product

Resources

About