Learning invariance from transformation sequences
Neural Computation
Action Recognition Using a Bio-Inspired Feedforward Spiking Network
International Journal of Computer Vision
Activity recognition by integrating the physics of motion with a neuromorphic model of perception
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
The human action image and its application to motion recognition
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
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Humans can recognize biological motion from strongly impoverished stimuli, like point-light displays. Although the neural mechanism underlying this robust perceptual process have not yet been clarified, one possible explanation is that the visual system extracts specific motion features that are suitable for the robust recognition of both normal and degraded stimuli. We present a neural model for biological motion recognition that learns robust mid-level motion features in an unsupervised way using a neurally plausible memory-trace learning rule. Optimal mid-level features were learnt from image motion sequences containing a walker with, or without background motion clutter. After learning of the motion features, the detection performance of the model substantially increases, in particular in presence of clutter. The learned mid-level motion features are characterized by horizontal opponent motion, where this feature type arises more frequently for the training stimuli without motion clutter. The learned features are consistent with recent psychophysical data that indicates that opponent motion might be critical for the detection of point light walkers.