Novel Quantum-Integrated CNN Model for Improved Human Activity Recognition in Smart Surveillance

Tanvir Fatima Naik Bukht^1,2, Yanfeng Wu¹, Nouf Abdullah Almujally³, Shuoa S. AItarbi⁴, Hameedur Rahman², Ahmad Jalal^2,5,*, Hui Liu^1,6,7,*
1 Guodian Nanjing Automation Co., Ltd., Nanjing, 210032, China
2 Department of Computer Science, Air University, E-9, Islamabad, 44000, Pakistan
3 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
4 Department of Cyber Security, College of Humanities, Umm Al-Qura University, Mecca, 24381, Saudi Arabia
5 Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul, 02841, Republic of Korea
6 School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
7 Cognitive Systems Lab, University of Bremen, Bremen, 28359, Germany
* Corresponding Author: Ahmad Jalal. Email: email ; Hui Liu. Email: email
(This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.071850

Received 13 August 2025; Accepted 06 November 2025; Published online 28 November 2025

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Abstract

Human activity recognition (HAR) is crucial in fields like robotics, surveillance, and healthcare, enabling systems to understand and respond to human actions. Current models often struggle with complex datasets, making accurate recognition challenging. This study proposes a quantum-integrated Convolutional Neural Network (QI-CNN) to enhance HAR performance. The traditional models demonstrate weak performance in transferring learned knowledge between diverse complex data collections, including D3D-HOI and Sysu 3D HOI. HAR requires better extraction models and techniques that must address current challenges to achieve improved accuracy and scalability. The model aims to enhance HAR task performance by combining quantum computing components with classical CNN approaches. The framework begins with bilateral filter (BF) enhancement of images and then implements multi-object tracking (MOT) in conjunction with felzenszwalb superpixel segmentation for object detection and segmentation. The watershed algorithm refines the united superpixels to create more accurate object boundary definitions. The model combination of histogram of oriented gradients (HoG) and Global Image Semantic Texture (GIST) descriptors alongside a new approach to extract 23-joint keypoints by employing relative joint angles and joint proximity measures. A fuzzy optimization process optimizes features that originated from the extraction phase. Our approach achieves 93.02% accuracy on the D3D-HOI dataset and 97.38% on the Sysu 3D HOI dataset Our approach achieves 93.02% accuracy on the D3D-HOI dataset and 97.38% on the Sysu 3D HOI dataset. Averaging across all classes, the proposed model yields 93.3% precision, 92.6% recall, 92.3% F1-score, 89.1% specificity, an False Positive Rate (FPR) of 10.9% and a mean log-loss of 0.134 on the D3D-HOI dataset, while on the Sysu 3D HOI dataset the corresponding values are 98.4% precision, 98.6% recall, 98.4% F1-score, 99.0% specificity, 1.0% FPR and a log-loss of 0.058. These results demonstrate that the quantum integrated CNN significantly improves feature extraction and model optimisation.