Neuro-probabilistic model for object tracking

Mondal, Ajoy

doi:10.1007/s10044-019-00791-6

Cited by 5 publications

(2 citation statements)

References 60 publications

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“…In [33], occlusion is one of the major challenges for object tracking in real-life scenarios. An effective observation model is proposed based on the confidence (classification) score provided to optimize the particle filter algorithm.…”

Section: Potential Methods For Casamentioning

confidence: 99%

A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis

Zhao¹,

Ma²,

Li³

et al. 2022

Preprint

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The Computer Assisted Sperm Analysis (CASA) plays a crucial role in male reproductive health diagnosis and Infertility treatment. With the development of the computer industry in recent years, a great of accurate algorithms are proposed. With the assistance of those novel algorithms, it is possible for CASA to achieve a faster and higher quality result. Since image processing is the technical basis of CASA, including pre-processing,feature extraction, target detection and tracking, these methods are important technical steps in dealing with CASA. The various works related to Computer Assisted Sperm Analysis methods in the last 30 years (since 1988) are comprehensively introduced and analysed in this survey. To facilitate understanding, the methods involved are analysed in the sequence of general steps in sperm analysis. In other words, the methods related to sperm detection (localization) are first analysed, and then the methods of sperm tracking are analysed. Beside this, we analyse and prospect the present situation and future of CASA. According to our work, the feasible for applying in sperm microscopic video of methods mentioned in this review is explained. Moreover, existing challenges of object detection and tracking in microscope video are potential to be solved inspired by this survey.

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Section: Potential Methods For Casamentioning

confidence: 99%

A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis

Zhao¹,

Ma²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Occlusion is a typical problem in multi-object tracking, and it is also one of the major challenges for object tracking in real-life scenarios [1]. Researchers often use feature similarity to solve occlusion problems in multi-object tracking, such as color features, contour features, and introducing attention mechanisms [2].…”

Section: Introductionmentioning

confidence: 99%

DetTrack: An Algorithm for Multiple Object Tracking by Improving Occlusion Object Detection

Gao,

Wang,

Wang

et al. 2023

Electronics

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Multi-object tracking (MOT) is an important problem in computer vision that has a wide range of applications. Currently, object occlusion detecting is still a serious challenge in multi-object tracking tasks. In this paper, we propose a method to simultaneously improve occluded object detection and occluded object tracking, as well as propose a tracking method for when the object is completely occluded. First, motion track prediction is utilized to improve the upper limit of occluded object detection. Then, the spatio-temporal feature information between the object and the surrounding environment is used for multi-object tracking. Finally, we use the hypothesis frame to continuously track the completely occluded object. Our study shows that we achieve competitive performances compared to the current state-of-the-art methods on popular multi-object tracking benchmarks such as MOT16, MOT17, and MOT20.

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Saliency information and mosaic based data augmentation method for densely occluded object recognition

Tong,

Luo,

et al. 2024

Pattern Anal Applic

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Neuro-probabilistic model for object tracking

Cited by 5 publications

References 60 publications

A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis

A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis

DetTrack: An Algorithm for Multiple Object Tracking by Improving Occlusion Object Detection

Saliency information and mosaic based data augmentation method for densely occluded object recognition

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