Multi-player detection in soccer broadcast videos using a blob-guided particle swarm optimization method

Manafifard, M.; Ebadi, Hamid; Moghaddam, Hamid Abrishami

doi:10.1007/s11042-016-3625-6

Cited by 18 publications

(13 citation statements)

References 40 publications

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“…This evaluation includes the comparison of our features with state of art human detection histogram of oriented gradients (HOG) features [6] which are still used in many state of the art player detection algorithms [1,15]. [7,29,15] have also performed this comparison analysis. HOG is trained on grey scale images for classification of occluded and non-occluded blobs.…”

Section: Resultsmentioning

confidence: 99%

“…It should be noted that everyone has used their own local datasets and hence no direct comparison is possible. [27,17,29,14] have used broadcast videos for the detection of players. We are performing estimation of number of players rather than just detection.…”

Section: Resultsmentioning

confidence: 99%

“…They perform 3-D reconstruction and deal with occlusion based on clustering and tracking of the 3D point clouds. Manafifard et al [29] suggested a detector that performs two-step blob detection (grass-based blob detection followed by an edge-based blob detection). They handled occlusion by a blob-guided PSO multi-player detection algorithm.…”

Section: Occlusion Handlingmentioning

confidence: 99%

See 2 more Smart Citations

Estimating the Number of Soccer Players Using Simulation-Based Occlusion Handling

Huda

Jensen

Gade

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Estimating the number of soccer players is crucial information for occupancy analysis and other monitoring activities in sports analysis. It depends on player detection in the field that should be independent of the environment and light conditions. Thermal cameras are therefore a better option over normal RGB cameras. Detection of nonoccluded players is doable but precise estimation of number of the players in groups is hard to achieve. Here we propose a novel method for estimating number of the players in groups using computer graphics and virtual simulations. Occlusion conditions are first classified by using distinctive set of features trained by a bagged tree classifier. Estimation of the number of players is then performed by maximum likelihood of probability density based approach to further classify the occluded players. The results show that the implemented strategy is capable of providing precise results even during occlusion conditions.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Occlusion Handlingmentioning

confidence: 99%

See 1 more Smart Citation

Estimating the Number of Soccer Players Using Simulation-Based Occlusion Handling

Huda

Jensen

Gade

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…Note that P pos vtarget and P neg vtarget are calculated by utilizing the output value of Eq. (8). Specifically, the sigmoid function is used as an activation function for f svm (v target ) according to [39].…”

Section: Second Phase : Multimodal Fusion Based On Confidence Measuresmentioning

confidence: 99%

“…Since soccer is one of the most popular sports worldwide, there is a strong demand for soccer video analysis techniques. Studies have been conducted on soccer player tracking [4][5][6][7][8], tactics analysis [9][10][11][12][13][14], event detection [15][16][17][18][19][20][21][22][23] and video summarization [24][25][26][27][28]. A soccer match takes about 100 minutes.…”

Section: Introductionmentioning

confidence: 99%

[Papers] Multimodal Important Scene Detection in Far-view Soccer Videos Based on Single Deep Neural Architecture

Haruyama¹,

Takahashi

Ogawa

et al. 2020

MTA

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The details of the matches of soccer can be estimated from visual and audio sequences, and they correspond to the occurrence of important scenes. Therefore, the use of these sequences is suitable for important scene detection. In this paper, a new multimodal method for important scene detection from visual and audio sequences in far-view soccer videos based on a single deep neural architecture is presented. A unique point of our method is that multiple classifiers can be realized by a single deep neural architecture that includes a Convolutional Neural Network-based feature extractor and a Support Vector Machine-based classifier. This approach provides a solution to the problem of not being able to simultaneously optimize different multiple deep neural architectures from a small amount of training data. Then we monitor confidence measures output from this architecture for the multimodal data and enable their integration to obtain the final classification result.

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Tracking of moving athlete from video sequences using flower pollination algorithm

et al. 2021

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Multi-player detection in soccer broadcast videos using a blob-guided particle swarm optimization method

Cited by 18 publications

References 40 publications

Estimating the Number of Soccer Players Using Simulation-Based Occlusion Handling

Estimating the Number of Soccer Players Using Simulation-Based Occlusion Handling

[Papers] Multimodal Important Scene Detection in Far-view Soccer Videos Based on Single Deep Neural Architecture

Tracking of moving athlete from video sequences using flower pollination algorithm

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