Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart home

Jalal, Ahmad; Uddin, Md. Zia; Kim, T.-S

doi:10.1109/tce.2012.6311329

Cited by 174 publications

(88 citation statements)

References 28 publications

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“…It determines the color in the image can appear most color number or the maximum gray level in the gray image. Image content understanding depth estimation method is mainly through classifying every scene in the image block, and then for each category of scenery with respective applicable method to estimate the depth of their information [23][24][25][26][27].…”

Section: The Intensity and Depth Pixels Problemmentioning

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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Section: The Intensity and Depth Pixels Problemmentioning

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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“…where, P(O | h l ) denoted the probability of likelihood of the h activity HMM among different number of activities [26][27][28]. Fig.…”

Section: Modified Hidden Markov Model (M-hmm)mentioning

confidence: 99%

Depth Images-based Human Detection, Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM

Kamal¹,

Jalal²,

Kim³

2016

Journal of Electrical Engineering and Technology

103

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-Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

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“…This means that human activities can be captured through video sensors installed in the local infrastructure [34]. Typical use of video sensors for in-situ lifelogging also includes work as reported in [18,16,1,49,17] and [2]. [18] proposed a depth video-based activity recognition system for smart spaces based on feature transformation and HMM recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Typical use of video sensors for in-situ lifelogging also includes work as reported in [18,16,1,49,17] and [2]. [18] proposed a depth video-based activity recognition system for smart spaces based on feature transformation and HMM recognition. Similar technologies are applied in other work by the same authors in [16] and [1] which can recognise human activities from body depth silhouettes.…”

Section: Introductionmentioning

confidence: 99%

Characterizing everyday activities from visual lifelogs based on enhancing concept representation

Wang

Sun

Smeaton

et al. 2016

Computer Vision and Image Understanding

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The proliferation of wearable visual recording devices such as SenseCam, Google Glass, etc. is creating opportunities for automatic analysis and usage of digitally-recorded everyday behaviour, known as visual lifelogs. Such information can be recorded in order to identify human activities and build applications that support assistive living and enhance the human experience. Although the automatic detection of semantic concepts from images within a single, narrow, domain has now reached a usable performance level, in visual lifelogging a wide range of everyday concepts are captured by the imagery which vary enormously from one subject to another. This challenges the performance of automatic concept detection and the identification of human activities because visual lifelogs will have such variety of semantic concepts across individual subjects. In this paper, we characterize the everyday activities and behaviour of subjects by applying a hidden conditional random field (HCRF) algorithm on an enhanced representation of semantic concepts appearing in visual lifelogs. This is carried out by first extracting latent features of concept occurrences based on weighted non-negative tensor factorization (WNTF) to exploit temporal patterns of concept occurrence. These results are then input to an HCRF-based model to provide an automatic annotation of activity sequences from a visual lifelog. Results for this are demonstrated in experiments to show the efficacy of our algorithm in improving the accuracy of characterizing everyday activities from individual lifelogs. The overall contribution is a demonstration that using images taken by wearable cameras we can capture and characterize everyday behaviour with a level of accuracy that allows useful applications which measure, or change that behaviour, to be developed.

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Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart home

Cited by 174 publications

References 28 publications

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

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

Depth Images-based Human Detection, Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM

Characterizing everyday activities from visual lifelogs based on enhancing concept representation

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