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

Chiu, Sheng-Yi; Chiu, Chung‐Cheng; Xu, Sendren Sheng-Dong

doi:10.3390/app8060885

Cited by 23 publications

(17 citation statements)

References 27 publications

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“…Specificity (a T n fraction) is the proportion of actual negatives predicted as negatives, sensitivity (a T p fraction) the proportion of actual positives predicted as positives, G-mean the root of the product of specificity and sensitivity, and accuracy the proportion of true results obtained, either T n or T p . The mathematical evaluation measures of the aforementioned metrics are shown in Equations ( 15) to (20) [17,56].…”

Section: Selection Of Srd Metricmentioning

confidence: 99%

Specular Reflection Detection and Inpainting in Transparent Object through MSPLFI

2021

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Multispectral polarimetric light field imagery (MSPLFI) contains significant information about a transparent object’s distribution over spectra, the inherent properties of its surface and its directional movement, as well as intensity, which all together can distinguish its specular reflection. Due to multispectral polarimetric signatures being limited to an object’s properties, specular pixel detection of a transparent object is a difficult task because the object lacks its own texture. In this work, we propose a two-fold approach for determining the specular reflection detection (SRD) and the specular reflection inpainting (SRI) in a transparent object. Firstly, we capture and decode 18 different transparent objects with specularity signatures obtained using a light field (LF) camera. In addition to our image acquisition system, we place different multispectral filters from visible bands and polarimetric filters at different orientations to capture images from multisensory cues containing MSPLFI features. Then, we propose a change detection algorithm for detecting specular reflected pixels from different spectra. A Mahalanobis distance is calculated based on the mean and the covariance of both polarized and unpolarized images of an object in this connection. Secondly, an inpainting algorithm that captures pixel movements among sub-aperture images of the LF is proposed. In this regard, a distance matrix for all the four connected neighboring pixels is computed from the common pixel intensities of each color channel of both the polarized and the unpolarized images. The most correlated pixel pattern is selected for the task of inpainting for each sub-aperture image. This process is repeated for all the sub-aperture images to calculate the final SRI task. The experimental results demonstrate that the proposed two-fold approach significantly improves the accuracy of detection and the quality of inpainting. Furthermore, the proposed approach also improves the SRD metrics (with mean F1-score, G-mean, and accuracy as 0.643, 0.656, and 0.981, respectively) and SRI metrics (with mean structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean squared error (IMMSE), and mean absolute deviation (MAD) as 0.966, 0.735, 0.073, and 0.226, respectively) for all the sub-apertures of the 18 transparent objects in MSPLFI dataset as compared with those obtained from the methods in the literature considered in this paper. Future work will exploit the integration of machine learning for better SRD accuracy and SRI quality.

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Section: Selection Of Srd Metricmentioning

confidence: 99%

Specular Reflection Detection and Inpainting in Transparent Object through MSPLFI

2021

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“…It assures if the two face images are concerning to the same human or not. The weighted Chisquare can be defined as following [16]: (4) B is defined as the number of bins in the image histogram. G and E are the comparing normalized histograms for the detected face image and a stored one at the training dataset.…”

Section: Fig 6: Example Of Lbph Codementioning

confidence: 99%

“…So, all of the innovative techniques are configured to prevent the intrusion threats without any efforts from the operators. Background subtraction approach is used with Gaussian mixture model to detect the motion of objects at the monitoring area [4]. One of the features extraction techniques can use to recognize the detected faces.…”

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confidence: 99%

Detection and Recognition of Moving Objects for Physical Systems Protection in Nuclear Facilities

Gaber

Khalaf

Mahmoud

et al. 2021

Journal of Advanced Engineering Trends

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Recently, the nuclear applications and services are entering in several domains of our life. It is a mandatory direction to protect the physical systems against any malicious sabotage or threat. Human activity detection and recognition techniques have a significant role in the physical protection systems. It enhances the protection regime through anomalous activity detection and authorized human-computer interaction applications. This paper is aimed to demonstrate a real-time system for moving object\human detection and recognition in order to protect the physical systems. It integrates computer vision with Internet of Things (IoT) technologies for detecting and recognizing the moving humans with their occupation times at the camera field. The proposed system can introduce more reliable and active intrusion detection scenarios with simple and low cost implementation techniques. It provides high flexibility for securing access to the monitoring information with storing for the historian purposes. Through an internet environment, the system can monitor the sensitive areas through detecting the unknown faces and their occupation times via ThingSpeak channels. The monitoring information is tracked through web based dashboards and ThingView mobile application. The system can launch automatically the alert notifications through SMS, and e-mail messages. These notifications are received whenever detecting an intruder face and exceeding the occupation times of human motion. The results provide effective features for implementing real-time and low cost applications even with low resolution cameras.

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“…The experimental results showed the capability and robustness of recognizing objects. In [52], an initial background extraction algorithm called Entropy-Based initial Background Extraction (EBBE) was proposed for poor initial-background conditions. The EBBE algorithm reduces the generation of false foregrounds in circumstances where there are large color disturbances due to complex conditions (e.g., camera shake, shaking trees, and snowflakes, or due to a very low frame rate).…”

Section: Related Workmentioning

confidence: 99%

“…Experiments were carried out on different image sequences, which are taken in matches played in FIFA World Cup Russia 2018. Our algorithm has been compared to six different algorithms, as shown in Table 5; they are Modified Atherton Algorithm (MAA) [50], Scale-Invariant Feature Transform based Algorithm (SIFT) [21], Speeded Up Robust Features based Algorithm (SURF) [51], Entropy-Based initial Background Extraction (EBBE) Algorithm [52], Improved Non-Maximum Suppression (INMS) [53] and Cuckoo Search Algorithm [54]. Supposing that the camera of the quadcopter is at a fixed height, the ball will correspond to a circular region with radii in the range (RMIN = 24, RMAX = 28) depending on the distance of the ball with respect to the optical center of the camera.…”

Section: Competitors and The Evaluation Image Sequencesmentioning

confidence: 99%

A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engine

Abulwafa

Saleh

Saraya

et al. 2021

Int J of Intelligent Sys

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Sports video analysis has received much attention as it is turned to be a hot research area in the field of image processing. This led to opportunities to develop fascinating applications supported by analysis of different sports especially football. Identifying the ball in soccer images is an essential task for not only goal scoring but also players' evaluation. However, soccer ball detection suffers from several hurdles such as; occlusions, fast moving objects, shadows, poor lighting, color contrast, and other static background objects. Although several ball detection techniques have been introduced such as; Frame Difference, Mixture of Gaussian (MoG), Optical Flow and etc., ball detection in soccer games is still an open research area. In this paper, a new Fuzzy Based Ball Detection (FB 2 D) strategy is proposed for identifying the ball through a set of image sequences extracted form a soccer match video. FB 2 D has the ability to accurately identify the ball even if it is attached to the white lines drawn on the playground or partially occluded behind players. FB 2 D has been compared to recent ball detection techniques. Experimental results have shown that FB 2 D outperforms recent detection techniques as it introduced the maximum accuracy and the accuracy of detection in the testing stage is close to 100%. As well as the minimum error.

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

Cited by 23 publications

References 27 publications

Specular Reflection Detection and Inpainting in Transparent Object through MSPLFI

Specular Reflection Detection and Inpainting in Transparent Object through MSPLFI

Detection and Recognition of Moving Objects for Physical Systems Protection in Nuclear Facilities

A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engine

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