Codebook hardware implementation on FPGA for background subtraction

Rodríguez-Gómez, Rafael A.; Fernandez-Sanchez, Enrique J.; Díaz, Javier; Ros, Eduardo

doi:10.1007/s11554-012-0249-6

Cited by 24 publications

(11 citation statements)

References 18 publications

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“…These results are proved later by Rodriguez-Gomez et al [21] and Szwoch et al [22] who compared various implementations of background subtraction systems. In their works, they define metrics to compare the efficiencies of such systems in terms of resources.…”

Section: Foreground/background Segmentationmentioning

confidence: 66%

Action boundaries detection in a video

Wehbe

Haidar

Joly

2015

Multimed Tools Appl

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. AbstractIn the video analysis domain, automatic detection of actions performed in a recorded video represents an important scientific and industrial challenge. This paper presents a new method to approximate the boundaries of actions performed by a person while interacting with his environment (such as moving objects). This method relies on a Codebook quantization method to analyze the rough evolution of each pixel and then decide whether this evolution corresponds to an action or not; this decision is taken by an automated system. Statistics are then produced -at the scale of the whole frame -to estimate the start and the end of an action. According to our proposed evaluation protocol, this method produces interesting results on both real and simulated videos. This statistic-based protocol is discussed at the end of this paper. The interpretation of this evaluation protocol nominates this method to be a solid base to localize the exact boundaries of actions or -in the framework of this research activity -to associate prescriptive text with a visual content.

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Section: Foreground/background Segmentationmentioning

confidence: 66%

Action boundaries detection in a video

Wehbe

Haidar

Joly

2015

Multimed Tools Appl

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“…In addition, the color-based algorithm has been previously implemented in real-time on FPGA (Field-Programmable Gate Array) [9], the Depth-Extended Codebook being suitable for embedded systems and smart cameras.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, there are many works in the literature focused on it: simple models for static backgrounds [1–3] or more advanced methods capable of dealing with dynamic backgrounds, such as MOG (Mixture of Gaussians) [4–6], Bayesian decision rules [7], the Codebook-based model [8,9], Kernel density estimation [10] or Component Analysis (PCA, Principal Component Analysis, and ICA, Independent Component Analysis) [11,12]. …”

Section: Introductionmentioning

confidence: 99%

“…Although other authors have already studied the inclusion of depth cues in background subtraction models, the presented work is innovative in different points:

It uses a high performance and low cost depth sensor, which directly provides accurate and dense depth estimations.

It is based on the Codebook model, which has been naturally generalized to integrate depth estimations. This model offers a good trade-off between accuracy and efficiency and can be reasonably extended to use more kinds of features [9,29,30]. We propose a novel method to integrate depth and color information.

…”

Section: Introductionmentioning

confidence: 99%

“…This model offers a good trade-off between accuracy and efficiency and can be reasonably extended to use more kinds of features [9,29,30]. We propose a novel method to integrate depth and color information.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Background Subtraction Based on Color and Depth Using Active Sensors

Fernandez-Sanchez

Díaz

Ros

2013

Sensors

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Depth information has been used in computer vision for a wide variety of tasks. Since active range sensors are currently available at low cost, high-quality depth maps can be used as relevant input for many applications. Background subtraction and video segmentation algorithms can be improved by fusing depth and color inputs, which are complementary and allow one to solve many classic color segmentation issues. In this paper, we describe one fusion method to combine color and depth based on an advanced color-based algorithm. This technique has been evaluated by means of a complete dataset recorded with Microsoft Kinect, which enables comparison with the original method. The proposed method outperforms the others in almost every test, showing more robustness to illumination changes, shadows, reflections and camouflage.

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