A deep survey on supervised learning based human detection and activity classification methods

Khan, Muhammad Attique; Mittal, Mamta; Goyal, Lalit Mohan; Roy, Sudipta

doi:10.1007/s11042-021-10811-5

Cited by 28 publications

(10 citation statements)

References 293 publications

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“…This method finds extensive use in face recognition, yielding favorable results [ 31 , 32 , 33 ]. Histograms of oriented gradient (HOG), an algorithm for extracting feature histograms from local pixel blocks [ 34 ], have enjoyed significant success in object detection, particularly within pedestrian detection scenarios [ 35 , 36 , 37 ]. LBP boasts advantages in rotation and grayscale invariance, effectively capturing image texture features, while HOG excels in capturing local shape information, maintaining strong invariance to geometric and optical variations.…”

Section: Introductionmentioning

confidence: 99%

Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model

Miao,

Jia,

Huang

et al. 2024

Cancer Biomarkers

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OBJECTIVES: This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images. METHODS: CT imaging data and clinical information were collected from 431 BC patients who underwent radical surgical resection at Harbin Medical University Cancer Hospital. The area of muscle and adipose tissue was obtained from CT images at the level of the eleventh thoracic vertebra. The corresponding histograms of oriented gradients (HOG) and local binary pattern (LBP) features were extracted from the CT images, and the network features were derived from the LBP and HOG features as well as the CT images through deep learning (DL). The combination of network features with clinical information was utilized to predict bone metastases in BC patients using the Gradient Boosting Decision Tree (GBDT) algorithm. Regularized Cox regression models were employed to identify independent prognostic factors for bone metastasis. RESULTS: The combination of clinical information and network features extracted from LBP features, HOG features, and CT images using a convolutional neural network (CNN) yielded the best performance, achieving an AUC of 0.922 (95% confidence interval [CI]: 0.843–0.964, P< 0.01). Regularized Cox regression results indicated that the subcutaneous fat index was an independent prognostic factor for bone metastasis in breast cancer (BC). CONCLUSION: Subcutaneous fat index could predict bone metastasis in BC patients. Deep learning multimodal algorithm demonstrates superior performance in assessing bone metastases in BC patients.

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Section: Introductionmentioning

confidence: 99%

Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model

Miao,

Jia,

Huang

et al. 2024

Cancer Biomarkers

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“…It attempts to discover the target visual features from randomly generated image sources. Some examples of applicable deployments include facial recognition [5]- [7] motion detection [8]- [11], image classification [12], [13], and vehicle detection [14]- [17]. Deep learning creates new opportunities for the development of intelligent interactions between people and their devices or technology, paving the path for these new possibilities to emerge.…”

Section: Introductionmentioning

confidence: 99%

Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme

Al-Selwi

Hassan

Ghani

et al. 2023

IJ-AI

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Deep learning and machine learning are becoming more extensively adopted artificial intelligence techniques for machine vision problems in everyday life, giving rise to new capabilities in every sector of technology. It has a wide range of applications, ranging from autonomous driving to medical and health monitoring. For image detection, the best reported approach is the you only look once (YOLO) algorithm, which is the faster and more accurate version of the convolutional neural network (CNN) algorithm. In the healthcare domain, YOLO can be applied for checking the face mask wearing of the people, especially in a public area or before entering any closed space such as a building to avoid the spread of the air-borne disease such as COVID-19. The main challenges are the image datasets, which are unstructured and may grow large, affecting the accuracy and speed of the detection. Secondly is the portability of the detection devices, which are generally dependent on the more portable like NVDIA Jetson Nano or from the existing computer/laptop. Using the low-power NVDIA Jetson Nano system as well as NVDIA giga texel shader extreme (GTX), this paper aims to design and implement real-time face mask wearing detection using the pre-trained dataset as well as the real-time data.

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“…A number of CPD-based methods have been applied to segment time series data into activities of interest—also known as ‘Activity Segmentation’. Algorithms are developed to identify the activity segments automatically and then the activity in those segments is identified through a recognition procedure [ 59 ]. Real-time activity recognition can be essential when identifying activities that require immediate care such as fall detection [ 60 ] or to automatically log behaviors for health monitoring.…”

Section: Introductionmentioning

confidence: 99%

The Lifespan of Human Activity Recognition Systems for Smart Homes

Hiremath,

Plötz

2023

Sensors

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With the growing interest in smart home environments and in providing seamless interactions with various smart devices, robust and reliable human activity recognition (HAR) systems are becoming essential. Such systems provide automated assistance to residents or to longitudinally monitor their daily activities for health and well-being assessments, as well as for tracking (long-term) behavior changes. These systems thus contribute towards an understanding of the health and continued well-being of residents. Smart homes are personalized settings where residents engage in everyday activities in their very own idiosyncratic ways. In order to provide a fully functional HAR system that requires minimal supervision, we provide a systematic analysis and a technical definition of the lifespan of activity recognition systems for smart homes. Such a designed lifespan provides for the different phases of building the HAR system, where these different phases are motivated by an application scenario that is typically observed in the home setting. Through the aforementioned phases, we detail the technical solutions that are required to be developed for each phase such that it becomes possible to derive and continuously improve the HAR system through data-driven procedures. The detailed lifespan can be used as a framework for the design of state-of-the-art procedures corresponding to the different phases.

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A deep survey on supervised learning based human detection and activity classification methods

Cited by 28 publications

References 293 publications

Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model

Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model

Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme

The Lifespan of Human Activity Recognition Systems for Smart Homes

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