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.

Inertial Sensor-based Daily Activity Recognition (IS-DAR) requires adaptable, data-efficient methods for effective multi-sensor use. This study presents an advanced detection system using body-worn sensors to accurately recognize activities. A structured pipeline enhances IS-DAR by applying signal preprocessing, feature extraction and optimization, followed by classification. Before segmentation, a Chebyshev filter removes noise, and Blackman windowing improves signal representation. Discriminative features—Gaussian Mixture Model (GMM) with Mel-Frequency Cepstral Coefficients (MFCC), spectral entropy, quaternion-based features, and Gammatone Cepstral Coefficients (GCC)—are fused to expand the feature space. Unlike existing approaches, the proposed IS-DAR system uniquely integrates diverse handcrafted features using a novel fusion strategy combined with Bayesian-based optimization, enabling a more accurate and generalized activity recognition. The key contribution lies in the joint optimization and fusion of features via Bayesian-based subset selection, resulting in a compact and highly discriminative feature representation. These features are then fed into a Convolutional Neural Network (CNN) to effectively detect spatial-temporal patterns in activity signals. Testing on two public datasets—IM-WSHA and ENABL3S—achieved accuracy levels of 93.0% and 92.0%, respectively. The integration of advanced feature extraction methods with fusion and optimization techniques significantly enhanced detection performance, surpassing traditional methods. The obtained results establish the effectiveness of the proposed IS-DAR system for deployment in real-world activity recognition applications.