Object-based analysis and interpretation of human motion in sports video sequences by dynamic bayesian networks

Luo, Ying; Wu, Tzong-Der; Hwang, Jenq‐Neng

doi:10.1016/j.cviu.2003.08.001

Cited by 83 publications

(58 citation statements)

References 28 publications

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“…An HMM is proven to be a special type of DBN with a fixed structure of inference graph. Luo et al [7] use DBNs to characterize the spatio-temporal nature of semantic video objects in several sport events, including downhill skiing, golf swing, baseball pitching, bowling, and ski jump. Moreover, a variation of DBN, named Coupled Hierarchical Duration-State DBN (CHDS-DBN), is proposed by Du et al [9] to model human interactions as a multiple stochastic process.…”

Section: Dynamic Bayesian Network (Dbn)mentioning

confidence: 99%

“…A DBN [78] is a Bayesian network with the same structure unrolled in the time axis as shown in Figure 15 [7]. An HMM is proven to be a special type of DBN with a fixed structure of inference graph.…”

Section: Dynamic Bayesian Network (Dbn)mentioning

confidence: 99%

“…The dynamic time warping (DTW) [14,16], a method for measuring similarity between two temporal sequences, which may vary in time or speed, is one of the most common temporal classification algorithms due to its simplicity; however, DTW is not appropriate for a large number of classes with many variations. Some probability-based methods by generative models (dynamic classifiers) are proposed such as Hidden Markov Models (HMM) [1,4,6,17,[75][76][77] and Dynamic Bayesian Networks (DBN) [7,9,78]. On the other hand, discriminative models (static classifiers) such as Support Vector Machine (SVM) [18,19,79,80], Relevant Vector Machine (RVM) [54,81,82] and Artificial Neural Network (ANN) [29,83,84], can also be used in this stage.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, in a healthcare system, the activity recognition can help the rehabilitation of patients, such as the automatic recognition of patient's action to facilitate the rehabilitation processes. There have been numerous research efforts reported for various applications based on human activity recognition, more specifically, home abnormal activity [1], ballet activity [2], tennis activity [3,4], soccer activity [5], human gestures [6], sport activity [7,8], human interaction [9], pedestrian traffic [10] and simple actions [11][12][13][14][15][16][17][18][19][20][21][22], and healthcare applications [1,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. In this paper, the video based technologies for human activity recognition will be extensively reviewed and discussed.…”

Section: Introductionmentioning

confidence: 99%

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A Review on Video-Based Human Activity Recognition

et al. 2013

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This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems. OPEN ACCESSComputers 2013, 2 89

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Section: Dynamic Bayesian Network (Dbn)mentioning

confidence: 99%

Section: Dynamic Bayesian Network (Dbn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review on Video-Based Human Activity Recognition

et al. 2013

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“…They showcase results on walking, jogging, running, hand waving and hand clapping activities. Authors in [20] employ DBNs for the semantic analysis of sports-related events in videos. The probabilistic behavior of human motion-related features has also been widely used through Support Vector Machines (SVMs).…”

Section: Related Workmentioning

confidence: 99%

Landmark-based multimodal human action recognition

Asteriadis

Daras

2016

Multimed Tools Appl

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Human activity recognition has received a lot of attention recently, mainly thanks to the advancements in sensing technologies and systems' increasing computational power. However, complexity in human movements, sensing devices' noise and person-specific characteristics impose challenges that still remain to be overcome. In the proposed work, a novel, multi-modal human action recognition method is presented for handling the aforementioned issues. Each action is represented by a basis vector and spectral analysis is performed on an affinity matrix of new action feature vectors. Using modality-dependent kernel regressors for computing the affinity matrix, complexity is reduced and robust lowdimensional representations are achieved. The proposed scheme supports online adaptivity of modalities, in a dynamic fashion, according to their automatically inferred reliability. Evaluation on three publicly available datasets demonstrates the potential of the approach.

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Deep Bayesian network architecture for Big Data mining

Njah

Jamoussi

Mahdi

2018

Concurrency and Computation

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Classical Datamining methods are facing various challenges in the era of Big Data. Between the need of fast knowledge extraction and the high flows of data acquired in small slots of time, these methods became shifted. The variability and the veracity of the Big Data perplex the Machine Learning process. The high volume of Big Data yields to a congested learning because the classic methods are designed for small sets of features. Deep Learning has recently emerged in the aim of handling voluminous data. The concept of the Deep induces the conversion of the features into a new abstracted representation in order to optimize an objective. Although the Deep Learning methods are experimentally promising, their parameterization is exhaustive and empirical.To tackle these problems, we utilize the causality and the uncertainty of the Bayesian Network in order to propose a new Deep Bayesian Network architecture. We provide a new learning algorithm for this multi-layered Bayesian Network with latent variables. We evaluate the proposed architecture and learning algorithms over benchmark datasets. We used high-dimensional data in order to simulate the Big Data challenges, which are imposed by the volume and veracity aspects. We demonstrate the effectiveness of our contribution under these constraints.

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Object-based analysis and interpretation of human motion in sports video sequences by dynamic bayesian networks

Cited by 83 publications

References 28 publications

A Review on Video-Based Human Activity Recognition

A Review on Video-Based Human Activity Recognition

Landmark-based multimodal human action recognition

Deep Bayesian network architecture for Big Data mining

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