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.

Group activity recognition in public environments is challenging due to dynamic formations, complex inter-person interactions, and frequent occlusions. Existing methods often emphasize individual actions, overlooking collective behavioral patterns. This work introduces a multi-modal framework integrating silhouette-based appearance and skeleton-based pose information for robust recognition in surveillance scenarios. You Only Look Once v11 (YOLOv11) detects persons, Segmenting Objects by LOcations version 2 (SOLOv2) segments instances, and AlphaPose extracts skeletons, followed by hierarchical grouping to form spatially coherent clusters. A hybrid feature extraction strategy combines handcrafted descriptors (Extended GIST (ExGIST), Distance Transform, Binary Robust Independent Elementary Features (BRIEF), Ridge) with deep representations, fused via multi-head attention. Feature selection is refined through a three-stage pipeline of Kernel Principal Component Analysis (K-PCA), mutual information ranking, and genetic algorithm-based optimization. Spatio-Temporal Graph Convolution Networks (ST-GCN) models spatio-temporal dependencies, while Long Short-Term Memory (LSTM) captures long-term dynamics for activity classification. On the Collective Activity Dataset (CAD), the framework achieves 96.80% accuracy, surpassing state-of-the-art approaches. Its modular design ensures scalability and adaptability for intelligent surveillance and smart city applications.