Multi-Camera Tracking Helmet System

Zhang, Zirui; Cheng, Jun

doi:10.12720/joig.1.2.76-79

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…Its expression is as follows:

v (x, y) = \frac{\underset{((), i, j \in Ω_{x, y})}{false\sum} w_{s} (i, j) • w_{r} (i, j) • u (i, j)}{\underset{((), i, j \in Ω_{x, y})}{false\sum} w_{s} (i, j) • w_{r} (i, j)}

w_{s} (i, j) = \exp (- \frac{{(||, i goodbreak- x)}^{2} + {(||, j goodbreak- y)}^{2}}{2 σ_{s}^{2}})

w_{r} (i, j) = \exp (- \frac{{|u (i, j) - v (x, y)|}^{2}}{2 σ_{r}^{2}})

where Ω x , y represent a spatial neighborhood pixel window which the size centered on ( x , y ) is (2 N + 1) × (2 N + 1), the larger the N , the better the smoothing effect. v ( x , y ) represents the center pixel value of Ω x , y , and u ( i , j ) represents the pixel value at ( i , j ) in the template; w s ( i , j ) is the spatial proximity factor; w r ( i , j ) is the gray similarity factor, w ( i , j ) = w s ( i , j ) × w r ( i , j ), which is the weight coefficient; σ s and σ r (Zhang et al, 2014), respectively control the attenuation degree of the weight factor in the spatial temporal progress and gray similarity. When σ s increases, more pixels participate in the weighting, and the image becomes blurred.…”

Section: Improved Algorithm and Implement Methodsmentioning

confidence: 99%

“…The method of combining wavelet transform and bilateral filter to reduce image noise has been applied in various practical scenes by many scholars (Chen et al, 2022; Qiang & Zhang, 2017; Xiao et al, 2016; Zhang et al, 2014). According to the characteristics of this kind of strong spot noise described in this paper, we proposed an improved wavelet transform and bilateral filtering algorithm for image processing.…”

Section: Introductionmentioning

confidence: 99%

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De‐noising method of aquatic product image with foreign matters based on improved wavelet transform and bilateral filter

Cui

2023

J Food Process Engineering

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Aiming at the strong spot noise caused by the reflection of meat slices in fish image with foreign matters that collected by ordinary camera or Single Lens Reflex (SLR) camera, we proposed an improved wavelet transform and bilateral filtering algorithm to reduce the noise of image. In this paper, our algorithm absorbed the advantages of wavelet threshold de‐noising algorithm and bilateral filtering algorithm, the wavelet threshold de‐noising algorithm being used for high frequency components and bilateral filtering being used for low frequency components, which not only achieved the effect of eliminating noise and weakening light spot, but also protected the details of the image. In the experiment, in order to verify the effectiveness of the proposed algorithm, we used the traditional bilateral filtering, Gaussian filtering and median filtering algorithms to process 500 salmon fish images which had residual fishbone, respectively. Since the original image itself was a noisy image, it was impossible to evaluate the noise reduction effect by using an algorithm that requires a noiseless image as a reference image, so we used five methods to evaluate the quality of the image processed by each algorithm, which were Blind/Referenceless Image Spatial QUality Evaluator (BRISQE), Cumulative Probability of Blur Detection (CPBD), variance, image entropy, and operation speed. Among them, BRISQE and CPBD could quantitatively reflect the overall quality of the image, the lower the BRISQE score or the higher the CPBD score, the better the quality of the image. Compared with other algorithms, the BRISQE score and CPBD score of the image processed by our algorithm were the best, with the average scores reaching 20.65 and .8421, respectively. Experimental results showed that the improved algorithm in this paper can effectively eliminate this kind of strong spot noise. The processed image has achieved better visual effect in both subjective and objective level.

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“…Its expression is as follows:

v (x, y) = \frac{\underset{((), i, j \in Ω_{x, y})}{false\sum} w_{s} (i, j) • w_{r} (i, j) • u (i, j)}{\underset{((), i, j \in Ω_{x, y})}{false\sum} w_{s} (i, j) • w_{r} (i, j)}

w_{s} (i, j) = \exp (- \frac{{(||, i goodbreak- x)}^{2} + {(||, j goodbreak- y)}^{2}}{2 σ_{s}^{2}})

w_{r} (i, j) = \exp (- \frac{{|u (i, j) - v (x, y)|}^{2}}{2 σ_{r}^{2}})

Section: Improved Algorithm and Implement Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

De‐noising method of aquatic product image with foreign matters based on improved wavelet transform and bilateral filter

Cui

2023

J Food Process Engineering

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show abstract

“…They accomplished this by fine-tuning the network using the initial 30 frames of the boxing videos and reported achieving an impressive mAP (mean Average Precision) of 0.95 across each frame. Moreover, researchers have experimented with various acquisition devices (multi-camera [14], thermal camera [15]), and have developed tracking approaches (region [16], spatiotemporal [17], heuristic [18]) to detect and track objects in complex environments [19].…”

Section: Introductionmentioning

confidence: 99%

AI-based, automated longitudinal performance monitoring of multiple boxers in large scale videos

Shanmugasundaramurthi,

Baghel,

Kirupakaran

et al. 2024

Fifth International Conference on Computer Vision and Computational Intelligence (CVCI 2024)

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Machine vision and AI-based techniques hold significant promise for automating the analysis of extensive sports video datasets to uncover longitudinal performance trends. This study introduces an innovative pipeline tailored for the analysis of lengthy top-view boxing training session videos, recorded in uncontrolled natural settings and featuring multiple athletes. Our primary focus lies in capturing the movement patterns of boxers within the ring. Within this research, we present Histotracker, an intelligent rule-based tracking module that connects segmented objects across frames using cosine similarity. Distinguishing itself from existing trackers, this module possesses the unique ability to backtrack and correlate frames with the highest association to maintain continuous tracking information. When compared to various standard approaches, our proposed Histotracker demonstrates remarkable results, boasting a MOTA score of 0.95 In approximately 69% of the total bout videos, there were no occurrences of Identity Switching or Identity Update. These findings hold immense promise for advancing the application of automated video analytics in diverse combat sports.

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“…In a general sense, target tracking is to estimate the trajectory of the target in the image sequence, as known as consistent labeling 1 . It has important research significance and excellent application prospects in the field of safety monitoring and intelligent driving 2,3 .…”

Section: Introductionmentioning

confidence: 99%

Track initiation algorithm for bearing-only target tracking in complex background

Wang

2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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Aiming at the difficulty of the target track initiation with bearing-only and no distance information under the complex background, an improved heuristic track initiation algorithm is proposed. Based on the motion characteristics of the target in the azimuth-pitch coordinate system, the motion trajectory of the target and clutter distribution are modeled.Combining the heuristic track initiation algorithm with Kalman filtering can build target track rapidly under complex background and ensure a higher probability of truth track. This method meets the full performance of the heuristic track initiation algorithm, and has a high probability of correct track in the case of multiple clutter numbers or high clutter density. Finally, the simulation is performed to verify the effectiveness of the proposed algorithm.

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Multi-Camera Tracking Helmet System

Cited by 13 publications

References 12 publications

De‐noising method of aquatic product image with foreign matters based on improved wavelet transform and bilateral filter

De‐noising method of aquatic product image with foreign matters based on improved wavelet transform and bilateral filter

AI-based, automated longitudinal performance monitoring of multiple boxers in large scale videos

Track initiation algorithm for bearing-only target tracking in complex background

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