Human-Body Feature Tracking for Speed and Reliability

The patent segments the tracking process into distinct modules: human-face region detection, human-body region determination, feature extraction, and trajectory matching. Each module processes specific information independently, allowing optimization of each component without compromising overall system performance. This segmentation enables the system to maintain high tracking effectiveness while improving speed by avoiding unnecessary computational operations in each module.

The patent applies partial action by extracting only the necessary features from the human-body region rather than processing the entire region. It uses a subset of historical object trajectories for matching purposes, and determines human-body regions based on expanded face regions rather than detecting all possible body parts. This selective approach reduces computational complexity while maintaining sufficient tracking accuracy.

The patent combines multiple tracking strategies into a composite system: it integrates human-face region detection with human-body region tracking, combines feature extraction with trajectory matching, and merges historical trajectory information with current frame data. This composite approach leverages the speed advantages of traditional algorithms while incorporating the effectiveness of deep learning through composite processing stages.

The patent introduces an intermediary feature extraction mechanism that bridges the gap between fast traditional algorithms and accurate deep learning methods. The feature extraction module acts as a mediator, processing visual information into compact representations that can be efficiently matched against historical trajectories, enabling both speed and accuracy.

The patent divides the processing system into multiple independent execution threads, each responsible for specific tasks: one thread handles human-face region detection, another handles human-body region determination, a third performs feature extraction, and a fourth handles trajectory matching. This segmentation allows parallel processing to improve efficiency while keeping each thread's logic simple and manageable.

The patent designs a universal processing framework where each execution thread follows the same basic structure and can be independently optimized. The threads share common data structures and control mechanisms, allowing the system to scale by adding more threads without proportionally increasing complexity. Each thread performs multiple functions within its designated task scope.