Latent Space Decomposition for Overlapping Action Recognition

The patent applies segmentation by dividing the pattern recognition task into distinct components: encoding input signals into latent representations, decomposing these representations into action-specific and non-action-specific components, and separately processing overlapping actions. This segmentation enables reliable detection of multiple simultaneous actions by treating them as independent detectable entities rather than a confused mixture.

The patent introduces latent space representations as an intermediary between raw input signals and final pattern recognition. This intermediate representation layer transforms complex overlapping action signals into disentangled latent components, making it easier to reliably detect and separate multiple simultaneous actions without direct complexity in the final recognition system.

The patent segments the action recognition process into encoding, decomposition, and detection stages. By decomposing latent representations into action-specific components, the system achieves precise measurement of overlapping actions through structured processing rather than direct analysis, improving precision while managing complexity through modular architecture.

The patent transitions from analyzing raw input signals directly to operating in a latent space dimension. This dimensional transformation allows for better separation and precise measurement of overlapping actions by representing them in a transformed feature space where their distinct characteristics are more easily distinguished and measured.

The patent performs preliminary encoding of input signals into latent representations and pre-decomposition into action-specific components before the actual pattern detection and segmentation tasks. This preliminary processing organizes the data in advance, making subsequent multi-label detection more reliable and efficient by presenting pre-processed, disintangled features to the detection algorithms.

The patent maintains continuous processing through the encoding-decomposition-detection pipeline, ensuring that useful information is preserved and transformed continuously rather than through discrete, interruptive steps. This continuous transformation through latent space representations maintains processing efficiency while improving detection reliability through systematic feature disentanglement.