Human Body Parts Tracking Using Torso Tracking: Applications to Activity Recognition

Dargazany, Aras; Nicolescu, Mircea

doi:10.1109/itng.2012.132

Cited by 21 publications

(8 citation statements)

References 8 publications

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“…Then, feature extraction is performed to describe the characteristics and movements of the segmented human objects. Different categories of features can be leveraged as features, including space-time volume [53,54], frequency [55], local descriptors [56] and body modeling [57,58]. The last step relies on the activity recognition and classification algorithms to recognize different kinds of human activities based on the features extracted above.…”

Section: Vision-based Human Activity Recognitionmentioning

confidence: 99%

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

et al. 2018

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This paper discusses how the state-of-the-art techniques in cyber-physical systems facilitate building smart warehouses to achieve the promising vision of industry 4.0. We focus on four significant issues when applying CPS techniques in smart warehouses. First, efficient CPS data collection: when limited communication bandwidth meets numerous CPS devices, we need to make more effort to study efficient wireless communication scheduling strategies. Second, accurate and robust localization: localization is the basis for many fundamental operations in smart warehouses, but still needs to be improved from various aspects like accuracy and robustness. Third, human activity recognition: human activity recognition can be applied in human-computer interaction for remote machine operations. Fourth, multi-robot collaboration: smart robots will take the place of humans to accomplish most tasks particularly in a harsh environment, and smart and fully-distributed robot collaborating algorithms should be investigated. Finally, we point out some challenging issues in the future CPS-based smart warehouse, which could open some new research directions.

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Section: Vision-based Human Activity Recognitionmentioning

confidence: 99%

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

et al. 2018

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“…In [39,60], algorithms are developed to detect the foreground, detect the blobs and track the blobs, which are used to represent head/hands. Nakazawa et al [61] use an ellipse to represent the human body and perform ellipse tracking by four steps, i.e., extraction of the human region from the image, generation of simulated image, matching and updating of human position.…”

Section: Model-freementioning

confidence: 99%

“…However, the above mentioned feature representations do not fully capture the whole body actions. Therefore, some human modeling methods [15,39,52,[60][61][62][63][64][65][66][67][68][69][70] are also proposed to model the human body including simple blobs, 2D body modeling and 3D body modeling. Generally, the body modeling requires the 2D/3D pose estimation problem.…”

Section: Introductionmentioning

confidence: 99%

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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“…The third step is extracted features and patterns are classified by using different classifiers such as SVM, KNN, histogram and template matching, etc. [6,7]. Machine learning techniques can be efficiently used for human activities detection in video.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Techniques to detect Real Time Human Activity using UCI Dataset

Arshad

Jaskani

Sabri³

et al. 2021

EAI Endorsed Trans IoT

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The cell phone is assuming a crucial job in present day life. It offers types of assistance and applications, for example, location tracking, medical applications, and human activity examination. All android smartphones have motion sensors i.e. Accelerometer, gyroscope, in order to detect motion of a user in a very precise way. In early conditions, committed sensors were utilized for activity acknowledgment. Different techniques are developed for distinguishing normal or human activities scenes in the crowd by processing the video or an image. A novel KNN-SVM human activity detection method is proposed to detect human activities in the UCI dataset for complex multi-process physical activities. Model trained with machine learning algorithms to capture the temporal dependency, normal sequences with high dimension is uniformly utilized to train the model to discriminate each activity. In the classification process, 2 different efficient classifiers are applied to identify the types of human activities in the UCI dataset. Support Vector Machine and K-Nearest Neighbour are applied in the proposed method for the classification. The efficiency of each classifiers is about 85% to 87%. The classification efficiency is comparable with existing literature after applying the majority decision in these classification techniques.

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Human Body Parts Tracking Using Torso Tracking: Applications to Activity Recognition

Cited by 21 publications

References 8 publications

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

CPS-Based Smart Warehouse for Industry 4.0: A Survey of the Underlying Technologies

A Review on Video-Based Human Activity Recognition

Hybrid Machine Learning Techniques to detect Real Time Human Activity using UCI Dataset

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