Joint Swing Energy for Skeleton-Based Gender Classification

Kwon, Beom; Lee, Sang Hoon

doi:10.1109/access.2021.3058745

Cited by 15 publications

(9 citation statements)

References 53 publications

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“…Joint Swing Energy (JSE) is a static feature extracted from the skeleton, namely the distance of the body joints from anatomical planes. It can easily be extracted from gait data and performs well with various classifiers to recognize someone’s gender ( Kwon and Lee, 2021 ). Histogram of Gradient (HG) method reduces the three-dimensional (3D) accelerometer and gyroscope data from smartphones into 1D temporal descriptors, used as input for a bootstrapped DT algorithm ( Jain and Kanhangad, 2018 ).…”

Section: Gait Based Biometricsmentioning

confidence: 99%

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

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We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

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Section: Gait Based Biometricsmentioning

confidence: 99%

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

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“…Instead of focusing on the face, other research works looked for cues from other parts, such as the body [3], and skeleton [5]. Exhibiting more exposure than face, the whole body area has been thought of as an opportune alternative despite its high variability in terms of appearance, poses, and scale.…”

Section: Related Workmentioning

confidence: 99%

“…Kwon et al [5] proposed a gait feature called joint swing energy (JSE) extracted from a sequence of 3D positions of skeletal joints to discriminate the gender. The intuition behind the JSE feature is that women and men walk differently which can be visualized from the pelvis and hip motion.…”

Section: Related Workmentioning

confidence: 99%

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Person Gender Classification on RGB-D Data With Self-Joint Attention

et al. 2021

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Automatic gender classification has many potential applications including automatic annotation of images, video surveillance, security, and human-computer interaction. In the last decades, many research works focused on classifying gender using the cues from 2D images of the person's frontal view. This limits their application in the real world. Also, classifying a person's gender from a different view, pose and scale are still challenging problems. RGB-D images, containing color and depth images, have more immunity to this problem than 2D images. Recent approaches using RGB-D images explored different combinations of features descriptors from depth and color images to classify the gender. These methods used lowlevel features extracted from the depth and color separately, ignoring the inter-dependency between the two modalities. In this article, we proposed a deep learning-based approach using a self-joint attention mechanism for human gender classification on RGB-D images. The proposed attention mechanism is designed to encode the inter-dependent information between the depth and color images to enhance the feature discrimination power. We benchmarked our method on a challenging gender dataset that consists of different views, poses, and scales. The presented method outperforms the state-of-art methods with an improvement in the accuracy of 5.2%, 7.5%, and 8.7% on three different test data.

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“…One of the most significant objects in video surveillance is a person since it is strongly associated with every event and activity like an intrusion, loiter, arson and abandonment. Therefore, many IVA functions have been extensively studied in terms of detecting and tracking a person [7], recognizing the appearance of his/her gender and age [14] and predicting upcoming future events [15]. In [16], the authors designed a new CNN model based on Fast R-CNN, which incorporates large and small sub-networks to detect various sizes of pedestrian instances in an image.…”

Section: Introductionmentioning

confidence: 99%

Toward an Online Continual Learning Architecture for Intrusion Detection of Video Surveillance

Kwon

Kim

2022

IEEE Access

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With recent advances in deep learning technologies, many commercialized video surveillance systems have adopted Artificial Intelligence (AI)-powered video analytics technologies as a way to make our life smarter and safer. Nevertheless, there is no robust architecture with an appropriate network model for commercial services considering both high accuracy and low computational cost. Existing deep learning technologies would not be enough to model and represent the dynamics of the real-world scene, so it is difficult to satisfy all environments using a generic model. Appropriate training data from false-alarm and/or missed cases can address this limitation but is rarely available due to legal issues relating to the privacy of personal data and the unpredictability of new incoming data. In this paper, we propose a novel end-to-end hybrid video surveillance architecture for reliable object detection, consisting of front-end and back-end intelligence. For the intelligent front-end, we propose a new object detector with a Multi-scale ResBlock scheme to consider the scalability and flexibility of the system. We are also developing a new domain adaptation method to replace the generic model with each camera's individual personal model by understanding real-time space and context information for intelligent back-end architecture. It is an iterative and continuous process in which new upcoming data and previous models are consistently engaged in a continuous improvement process. We conducted a series of experiments, including an interesting proof-ofconcept tests called the Chameleon project, which demonstrated the high accuracy and versatility of the new architecture, while producing robust results that can be implemented in practice.

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Joint Swing Energy for Skeleton-Based Gender Classification

Cited by 15 publications

References 53 publications

A Survey of Human Gait-Based Artificial Intelligence Applications

A Survey of Human Gait-Based Artificial Intelligence Applications

Person Gender Classification on RGB-D Data With Self-Joint Attention

Toward an Online Continual Learning Architecture for Intrusion Detection of Video Surveillance

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