Deep Learning

Monocular depth estimation from human imagery supports clinical gait analysis, sports biomechanics, surveillance, and human–computer interaction, yet practitioners lack direct evidence for selecting between OpenPose body-model variants. PoseDepth-CMP provides a locked-seed comparison of COCO (18 keypoints) and MPI (15 keypoints) within the same contour-guided geometric depth pipeline. On 240 controlled samples (seed 42), COCO achieves 94.4% keypoint accuracy with 13.0 cm depth error, whereas MPI achieves 87.3% accuracy with 15.4 cm error while reducing runtime by 13.3% and memory by 16.7%. The learned selector attains 93.6% accuracy and 12.4 cm depth error at intermediate computational cost.

Keywords: OpenPose · COCO keypoints · MPI keypoints · monocular depth estimation · human pose estimation · adaptive model selection

This research paper presents a novel integrated framework for semantic text understanding that bridges traditional Natural Language Processing (NLP) techniques with modern deep learning approaches. We address semantic ambiguity by proposing a hybrid methodology that leverages statistical methods and neural architectures. Through experimentation on multiple datasets, the approach achieves a 15% improvement in semantic similarity tasks and a 12% enhancement in named entity recognition accuracy compared to baselines.

Keywords: semantic understanding · hybrid NLP · deep learning · statistical methods · natural language processing