Shape and Motion Features Approach for Activity Tracking and Recognition from Kinect Video Camera

Jalal, Ahmad; Kamal, Shaharyar; Kim, Daijin

doi:10.1109/waina.2015.38

Cited by 95 publications

(45 citation statements)

References 17 publications

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“…In addition, we compare our method with the state of the art methods [31], [20], [35], [24], [21], [43]- [48] on the cross subject test setting and obtained a significantly improved recognition performance over existing works as shown in Table IX. To evaluate the recognition performance based on various codebook sizes, Fig.…”

Section: Methodsmentioning

confidence: 93%

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A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

Jalal¹,

Kamal²,

Kim³

2017

IJIMAI

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-Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR) realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs) to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition.

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Section: Methodsmentioning

confidence: 93%

“…Accuracy (%) Dynamic temporal warping [33] 54.0 Bag of 3D points [20] 74.7 HOJ3D [35] 79.0 Motion and Shape features [43] 82.1 Eigenjoints [24] 82.3 Semi Supervised learning [44] 83.5 Grassmannian manifold [45] 86.2 HON4D [21] 88.3 Pose Set [46] 90.0 HOD Descriptor [47] 91.2 Euclidean group algorithm [48] 92.4 Multi-Features method 93.1…”

Section: Methodsmentioning

confidence: 99%

A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

Jalal¹,

Kamal²,

Kim³

2017

IJIMAI

Self Cite

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“…In this paper, semantic space, and semantic-based features such as pose, poselet, related objects, attributes, and scene context are also defined and discussed. Different handcrafted features extraction and representation methods have been proposed for human action recognition [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

2017

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Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.

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“…In vision-based technology, the depth cameras become focusing area for many researchers in different HCI fields [14][15][16][17][18]. Many studies have shown that, in order to be able to study high-level abstractions of complex functions to solve the target recognition, speech perception and the language understanding such as the artificial intelligence related tasks, we need to adopt deep learning technique.…”

Section: Introductionmentioning

confidence: 99%

A Novel Face Recognition Algorithm based on the Deep Convolution Neural Network and Key Points Detection Jointed Local Binary Pattern Methodology

Huang¹,

Zhang²

2017

Journal of Electrical Engineering and Technology

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-This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

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Shape and Motion Features Approach for Activity Tracking and Recognition from Kinect Video Camera

Cited by 95 publications

References 17 publications

A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

A Novel Face Recognition Algorithm based on the Deep Convolution Neural Network and Key Points Detection Jointed Local Binary Pattern Methodology

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