GLBRF: Group-Based Lightweight Human Behavior Recognition Framework in Video Camera
Source abstract
Abstract
Behavioral recognition is an important technique for recognizing actions by analyzing human behavior. It is used in various fields, such as anomaly detection and health estimation. For this purpose, deep learning models are used to recognize and classify the features and patterns of each behavior. However, video-based behavior recognition models require a lot of computational power as they are trained using large datasets. Therefore, there is a need for a lightweight learning framework that can efficiently recognize various behaviors. In this paper, we propose a group-based lightweight human behavior recognition framework (GLBRF) that achieves both low computational burden and high accuracy in video-based behavior recognition. The GLBRF system utilizes a relatively small dataset to reduce computational cost using a 2D CNN model and improves behavior recognition accuracy by applying location-based grouping to recognize interaction behaviors between people. This enables efficient recognition of multiple behaviors in various services. With grouping, the accuracy was as high as 98%, while without grouping, the accuracy was relatively low at 68%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Anomaly Detection Techniques and Applications · Human Pose and Action Recognition · Video Surveillance and Tracking Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Software, Daegu Catholic University, Gyeongsan 38430, Republic of Korea
Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea
School of Computer Software, Daegu Catholic University, Gyeongsan 38430, Republic of Korea
Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, Republic of Korea
Funding
National Research Foundation of Korea (NRF) · Soonchunhyang University
References (35)
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