Video-Based Human Activity Recognition Using Deep Learning Approaches

Surek, Guilherme A. S.; Seman, Laio Oriel; Stefenon, Stéfano Frizzo; Mariani, Viviana Cocco; Coelho, Leandro dos Santos

doi:10.3390/s23146384

Cited by 19 publications

(3 citation statements)

References 84 publications

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“…Furthermore, within this realm, extensive exploration has been undertaken in smart grids [142][143][144] and the realm of the IoT [145]. Within this framework, machine learning stands as a subset of AI focused on constructing algorithms through data learning rather than predefined instructions [146]. Ensemble learning methods amalgamate multiple machine learning models [147], employing techniques like averaging or weighted averages, to address the limitations associated with relying solely on a single model [148].…”

Section: Artificial Intelligence Applicationsmentioning

confidence: 99%

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation

Buratto,

Muniz,

Nied

et al. 2024

Sensors

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This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

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Section: Artificial Intelligence Applicationsmentioning

confidence: 99%

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation

Buratto,

Muniz,

Nied

et al. 2024

Sensors

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“…These findings attest to the potential of Smart IMU, GPS, Audio, and Ambient Sensors in precisely identifying and classifying a range of human activities ( Gioanni et al, 2016 ). Beyond exploring deep learning techniques, this research paper introduces a hybrid system ( She et al, 2022 ; Liang et al, 2018 ; Liu et al, 2022d ; Vrskova et al, 2023 ; Surek et al, 2023 ) that blends machine learning and deep learning features. By capitalizing on the strengths of both paradigms, the hybrid system further sharpens activity recognition, signaling a promising avenue for future research and development.…”

Section: Introductionmentioning

confidence: 99%

Robust human locomotion and localization activity recognition over multisensory

Khan,

Alonazi,

Abdelhaq

et al. 2024

Front. Physiol.

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Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing smartphone IMU, Ambient, GPS, and Audio sensor data from two public benchmark datasets: the Opportunity dataset and the Extrasensory dataset. The Opportunity dataset was collected from 12 subjects participating in a range of daily activities, and it captures data from various body-worn and object-associated sensors. The Extrasensory dataset features data from 60 participants, including thousands of data samples from smartphone and smartwatch sensors, labeled with a wide array of human activities. Our study incorporates novel feature extraction techniques for signal, GPS, and audio sensor data. Specifically, for localization, GPS, audio, and IMU sensors are utilized, while IMU and Ambient sensors are employed for locomotion activity recognition. To achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been explored. For indoor/outdoor activities, CNNs are applied, while LSTMs are utilized for locomotion activity recognition. The proposed system has been evaluated using the k-fold cross-validation method, achieving accuracy rates of 97% and 89% for locomotion activity over the Opportunity and Extrasensory datasets, respectively, and 96% for indoor/outdoor activity over the Extrasensory dataset. These results highlight the efficiency of our methodology in accurately detecting various human activities, showing its potential for real-world applications. Moreover, the research paper introduces a hybrid system that combines machine learning and deep learning features, enhancing activity recognition performance by leveraging the strengths of both approaches.

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“…In this field, several authors are working to reduce the complexity of the classification step, considering the use of big data [41][42][43] and making this evaluation more efficient [44][45][46]. Finally, correlation-based methods compare features by similarity to determine whether they contain Duplication, often using a final step to eliminate a portion of the found correlations.…”

mentioning

confidence: 99%

Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks

Alencar,

Lopes,

Fernandes

et al. 2024

Future Internet

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In the current era of social media, the proliferation of images sourced from unreliable origins underscores the pressing need for robust methods to detect forged content, particularly amidst the rapid evolution of image manipulation technologies. Existing literature delineates two primary approaches to image manipulation detection: active and passive. Active techniques intervene preemptively, embedding structures into images to facilitate subsequent authenticity verification, whereas passive methods analyze image content for traces of manipulation. This study presents a novel solution to image manipulation detection by leveraging a multi-stream neural network architecture. Our approach harnesses three convolutional neural networks (CNNs) operating on distinct data streams extracted from the original image. We have developed a solution based on two passive detection methodologies. The system utilizes two separate streams to extract specific data subsets, while a third stream processes the unaltered image. Each net independently processes its respective data stream, capturing diverse facets of the image. The outputs from these nets are then fused through concatenation to ascertain whether the image has undergone manipulation, yielding a comprehensive detection framework surpassing the efficacy of its constituent methods. Our work introduces a unique dataset derived from the fusion of four publicly available datasets, featuring organically manipulated images that closely resemble real-world scenarios. This dataset offers a more authentic representation than other state-of-the-art methods that use algorithmically generated datasets based on image patches. By encompassing genuine manipulation scenarios, our dataset enhances the model’s ability to generalize across varied manipulation techniques, thereby improving its performance in real-world settings. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. Moreover, our proposed approach exhibits reduced accuracy variance compared to alternative models, underscoring its stability and robustness across diverse datasets. The approach outlined in this work needs to provide information about the specific location or type of tempering, which limits its practical applications.

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Video-Based Human Activity Recognition Using Deep Learning Approaches

Cited by 19 publications

References 84 publications

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation

Robust human locomotion and localization activity recognition over multisensory

Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks

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