Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 1442-1447· 0 citations· 19 references
Abstract
Human action recognition (HAR) plays a crucial role in safety monitoring, intelligent surveillance systems, and human-computer interaction applications. In this study, we evaluate and compare several deep learning architectures for HAR using the Weizmann dataset, using a YOLO-based preprocessing, including CNN, CNN with attention mechanism, MobileNetV2, and InceptionV3. The proposed YOLO-based preprocessing method was specifically designed to enhance feature extraction efficiency by isolating human subjects from background clutter, thereby reducing noise and improving spatial focus. Experimental results demonstrate that the YOLO-based CNN achieved state-of-the-art performance with an accuracy of 99.6%, significantly outperforming the CNN-Attention model (98.6%), MobileNetV2 (96.1%), and InceptionV3 (93.7%). These findings underscore the importance of robust preprocessing techniques and highlight the superiority of the proposed YOLO-based method in handling complex real-world scenarios.
Human Activity Recognition (HAR) is a fast-growing research area that focuses on identifying human actions using data collected from sensors and vision-based devices. It plays an important role in applications like health monitoring, smart homes, surveillance, sports analysis, and human-computer interaction. In recent...
A deep learning–based HAR framework utilizing hip-mounted accelerometer and gyroscope signals from the USC-HAD dataset, which contains readings from healthy participants only, is evaluated, providing a more realistic assessment of subject-independent generalization across unseen individuals.
F. Naveed, Hamza Khan, Zaki Uddin et al.· Scientific Reports· 0 citations
The article compares the performance of traditional machine learning techniques with recent deep learning architectures such as CNNs, RNNs, TCNs, and Transformers, based on accuracy, computational cost, and suitability for real-world disorderly plotting.
Disha Deotale, Madhushi Verma, P. Suresh et al.· Discover Artificial Intellig...· 0 citations
Human Activity Recognition (HAR) is a rapidly growing research field in computer vision and has various applications, such as intelligent surveillance systems, sports analysis, healthcare, and human-computer interaction. This study aims to analyze the performance of the YOLOv26 Classification model in recognizing video...
A resource-constrained CNN-GRU hybrid model for HAR on the WISDM dataset that uses convolutional layers for spatial learning and gated recurrent units (GRU) for sequence learning, enabling real-time HAR on edge devices.
H. S. Ganesha, K. G. Harsha, Roshini P et al.· International journal of com...· 0 citations
A compact and deployable Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) framework that combines a 2D convolutional backbone (AlexNet) for frame-level descriptors with an LSTM head for sequence modeling is proposed, indicating a robust, real-time-capable solution for video understanding in both offline a...
H. Khan, Altaf Hussain· ICCK Transactions on Advance...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
