NIC: A Robust Background Extraction Algorithm for Foreground Detection in Dynamic Scenes

Huynh‐The, Thien; Baños, Oresti; Lee, Sungyoung; Kang, Byeong Ho; Kim, Eun-Soo; Le-Tien, Thuong

doi:10.1109/tcsvt.2016.2543118

Cited by 39 publications

(14 citation statements)

References 52 publications

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“…In this section, the proposed method is described in detail. The background subtraction method is first developed and successfully applied in the optical video surveillance domain [18,19], a logarithm kind method can be found in [20], but such an idea is first introduced in SAR moving target detection by our team (to our knowledge) [11]. The main idea of the logarithm background subtraction method is simple.…”

Section: Methodsmentioning

confidence: 99%

Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

et al. 2019

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Spaceborne spotlight SAR mode has drawn attention due to its high-resolution capability, however, the studies about moving target detection with this mode are less. The paper proposes an image sequence-based method entitled modified logarithm background subtraction to detect ground moving targets with Gaofen-3 Single Look Complex (SLC) spotlight SAR images. The original logarithm background subtraction method is designed by our team for airborne SAR. It uses the subaperture image sequence to generate a background image, then detects moving targets by using image sequence to subtract background. When we apply the original algorithm to the spaceborne spotlight SAR data, a high false alarm problem occurs. To tackle the high false alarm problem due to the target’s low signal-to-noise-ratio (SNR) in spaceborne cases, several improvements are made. First, to preserve most of the moving target signatures, a low threshold CFAR (constant false alarm rate) detector is used to get the coarse detection. Second, because the moving target signatures have higher density than false detections in the coarse detection, a modified DBSCAN (density-based spatial-clustering-of-applications-with-noise) clustering method is then adopted to reduce false alarms. Third, the Kalman tracker is used to exclude the residual false detections, due to the real moving target signature having dynamic behavior. The proposed method is validated by real data, the shown results also prove the feasibility of the proposed method for both Gaofen-3 and other spaceborne systems.

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Section: Methodsmentioning

confidence: 99%

Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

et al. 2019

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“…The color information of neighboring points of the pixels, which were determined to be part of the background, was randomly included in background model updates. Huynh-The et al [22] proposed a background extraction algorithm called the neighbor-based intensity correction (NIC) method. NIC considers the first frame as an initial background.…”

Section: Related Workmentioning

confidence: 99%

A Background Subtraction Algorithm in Complex Environments Based on Category Entropy Analysis

Chiu

2018

Applied Sciences

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Background subtraction is a mainstream algorithm for moving object detection in video surveillance systems. It segments moving objects by using the difference between the background and input images. The key to background subtraction is to establish a reliable initial background. In this study, we propose a background subtraction algorithm based on category entropy analysis that dynamically creates color categories for each pixel in the images. The algorithm uses the concept of a joint category to build background categories that can adapt to the color disturbance of the background. Furthermore, in order to overcome dynamic background environments, this paper proposes the concept of color category entropy to estimate the number of necessary background categories and establish sufficient and representative background categories to adapt to dynamic background environments. In addition, recent mainstream methods for background subtraction were implemented and analyzed in comparison with our algorithm.

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“…These metrics are widely used to estimate the quality of background subtraction methods (Brutzer et al, 2011;Vacavant et al, 2012). For further evaluating our CPB and CPB+HoD, we introduce the peak signal-to-noise ratio (PSNR) as our metric (Huynh-Thu and Ghanbari, 2008), which can be used to measure the quality of the estimated resulted compared with the background truth (Huynh-The et al, 2016). The definition of PSNR is calculated as follows:…”

Section: Evaluation Measurementmentioning

confidence: 99%

Co-occurrence Background Model with Hypothesis on Degradation Modification for Robust Object Detection

Zhou

Kaneko

Hashimoto

et al. 2018

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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This paper presents a prospective background model for robust object detection in severe scenes. This background model using a novel algorithm, Co-occurrence Pixel-block Pairs (CPB), that extracts the spatiotemporal information of pixels from background and identifies the state of pixels at current frame. First, CPB realizes a robust background model for each pixel with spatiotemporal information based on a "pixel to block" structure. And then, CPB employs an efficient evaluation strategy to detect foreground sensitively, which is named as correlation dependent decision function. On the basis of this, a Hypothesis on Degradation Modification (HoD) for CPB is introduced to adapt dynamic changes in scenes and reinforce robustness of CPB to against "noise" in real conditions. This proposed model is robust to extract foreground against changes, such as illumination changes and background motion. Experimental results in different challenging datasets prove that our model has good effect for object detection.

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NIC: A Robust Background Extraction Algorithm for Foreground Detection in Dynamic Scenes

Cited by 39 publications

References 52 publications

Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

A Background Subtraction Algorithm in Complex Environments Based on Category Entropy Analysis

Co-occurrence Background Model with Hypothesis on Degradation Modification for Robust Object Detection

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