Abnormal Motion Analysis for Tracking-Based Approaches Using Region-Based Method with Mobile Grid

Casagrande, Jorge Henrique Busatto; Stemmer, Marcelo Ricardo

doi:10.12720/joig.2.1.22-27

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…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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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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“…Visual object tracking is a significant issue related to many practical applications, such as video surveillance, autonomous vehicle navigation, human-computer interaction and so on [1][2][3][4][5] . The tracking process typically selects the target object in a given initial frame and obtains the initial bounding box of the target, then sends the target information into the feature extraction operator for feature abstraction, and then automatically estimates the state of the moving object in the subsequent frames 2,7,8 .…”

Section: Introductionmentioning

confidence: 99%

An effective anti-interference visual tracking method

Fan

Yue²,

Zou³

2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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Tracking specific objects in images or videos is one of the most attractive problems in visual tasks. It is widely employed in security monitoring, automatic driving, military operations and other scenes. Recently, object tracker based on convolution neural network, especially Siamese network, obtains high accuracy and has been deeply studied. However, in practical application scenarios of visual tracking, when meets clutter background or the object is occluded, the accuracy of the tracking task will drop rapidly, and the tracker loses the target in extreme cases. It is particularly necessary to quickly and accurately relocate the target. Therefore, an anti-interference tracker based on Siamese convolution neural network is developed. Benefiting from the adaptive tracking confidence parameter, once the tracking effect of the tracker has dropped significantly during the tracking process, the location of the object will be corrected immediately. Experimental results show that the proposed method has the ability to relocate and track the target after occlusion or loss effectively.

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“…Local spatial positioning 4,11 can be divided into different positioning tar-gets: 1) The camera moves with the target in an unknown area for self-positioning, such as radio frequency identification(RFID) 5 , Wireless Fidelity(WiFi) 6 , Bluetooth 7 , ul-tra-wideband (UWB) 8 and based on inertial navigation simultaneous localization and mapping (SLAM) 9 ; 2) The camera is fixed to locate the target person or object in a known area. Current technical means include lidar 10 , computer vision 11 and so on.…”

Section: Introductionmentioning

confidence: 99%

Indoor target tracking with deep learning-based YOLOv3 model

Dong

xia²,

Zhao³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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For the problem of real-time indoor localization of workers in factory workshops and corridors, the pre-trained YOLOv3 detection model based on deep learning network is used to realize the visual localization of unmarked dynamic targets by monocular cameras. This method only needs a fixed-position camera in the measured area to complete real-time detection and localization of moving targets in the measured area. The algorithm is verified by simulation and experiment, and the personnel localization error of 8.2cm on the X axis and 19.57cm on the Y axis is obtained. Compared with other localization methods, it has the advantages of relatively low hardware cost, simple system setup, high algorithm portability, good practicability and industrial promotion value.

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Abnormal Motion Analysis for Tracking-Based Approaches Using Region-Based Method with Mobile Grid

Cited by 12 publications

References 9 publications

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

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

An effective anti-interference visual tracking method

Indoor target tracking with deep learning-based YOLOv3 model

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