A Dual Fast and Slow Feature Interaction in Biologically Inspired Visual Recognition of Human Action

TL;DR

This paper introduces a biologically inspired model that integrates fast motion features and slow form features for human action recognition, inspired by the dual pathways in the mammalian visual system, demonstrating promising results on standard datasets.

Contribution

The paper proposes a novel dual pathway model combining fast and slow feature interactions for human action recognition, inspired by neuroscience insights.

Findings

Achieved promising accuracy on KTH and Weizmann datasets.

Demonstrated effective integration of fast and slow features.

Validated the model's biological plausibility and performance.

Abstract

Computational neuroscience studies that have examined human visual system through functional magnetic resonance imaging (fMRI) have identified a model where the mammalian brain pursues two distinct pathways (for recognition of biological movement tasks). In the brain, dorsal stream analyzes the information of motion (optical flow), which is the fast features, and ventral stream (form pathway) analyzes form information (through active basis model based incremental slow feature analysis ) as slow features. The proposed approach suggests the motion perception of the human visual system composes of fast and slow feature interactions that identifies biological movements. Form features in the visual system biologically follows the application of active basis model with incremental slow feature analysis for the extraction of the slowest form features of human objects movements in the ventral stream. Applying incremental slow feature analysis provides an opportunity to use the action prototypes. To extract the slowest features episodic observation is required but the fast features updates the processing of motion information in every frames. Experimental results have shown promising accuracy for the proposed model and good performance with two datasets (KTH and Weizmann).