Advancements in Human Activity Recognition: Novel Shape Index Local Ternary Pattern and Hybrid Classifier Model for Enhanced Video Data Analysis - Kamble, Milind V.; Bichkar, Rajankumar (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Human activity recognition using video data is a fundamental yet challenging task in computer vision. This paper proposes a novel method for spatial feature extraction and classification model design to enhance the accuracy and robustness of human activity recognition systems. The primary proposed work is the development of the Shape Index Local Ternary Pattern, a novel spatial feature extraction method aimed at capturing intricate spatial relationships within video data. This feature significantly enhances the representation of spatial information, thereby overcoming the limitations of traditional texture-based features. Moreover, a Hybrid Classifier Model is proposed, comprising an initial layer integrating a Support Vector Machine and k-Nearest Neighbour classifiers. The outputs of these classifiers are fed into an Artificial Neural Network for the final classification. This hybrid approach combines the strengths of different classifiers and artificial neural network architectures, resulting in improved classification accuracy and robustness. The efficacy of the proposed method was rigorously evaluated and validated through extensive experiments conducted on UCF101 and HACS datasets. The results showed superior performance in accurately identifying a range of human activities. The Shape Index Local Ternary Pattern feature demonstrates its effectiveness in capturing intricate spatial details, whereas the hybrid model shows substantial advancements in classification accuracy and robustness.