Hybrid Quantum Gate Enabled CNN Framework with Optimized Features for Human-Object Detection and Recognition

Recognising human-object interactions (HOI) is a challenging task for traditional machine learning models, including convolutional neural networks (CNNs). Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI. The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity. HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability. This work proposes a Novel quantum gate-enabled hybrid CNN (QEH-CNN) for effective HOI recognition. The model enhances CNN performance by integrating quantum computing components. The framework begins with bilateral image filtering, followed by multi-object tracking (MOT) and Felzenszwalb superpixel segmentation. A watershed algorithm refines object boundaries by cleaning merged superpixels. Feature extraction combines a histogram of oriented gradients (HOG), Global Image Statistics for Texture (GIST) descriptors, and a novel 23-joint keypoint extraction method using relative joint angles and joint proximity measures. A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNN model. The proposed model achieves 95.06% accuracy on the 3D-D3D-HOI dataset and 97.29% on the SYSU 3D HOI dataset. The integration of quantum computing enhances feature optimization, leading to improved accuracy and overall model efficiency.