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Abstract
Humans can easily understand other people’s actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1), and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of the three-dimensional spatial-temporal correlative Gabor filters is used to model the dynamic properties of the classical receptive field of V1 simple cell tuned to different speeds and orientations in time for detection of spatiotemporal information from video sequences. Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information. Visual attention model based on perceptual grouping is integrated into our model to filter and group different regions. Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons. The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model.
Highlights
It is a universally accepted fact that human can recognize and understand other peoples action from complex natural scene
The feature vector HI computed in Eq (35), is dependent on different parameters, including subsequence length tmax, size of glide time window 4t, number of preferred speeds Nv and their values, et al To evaluate the performance of our model for action recognition, the following test experiments are firstly performed with different parameter settings
In this paper we propose a bio-inspired model to extract spatiotemporal features from videos for human action recognition
Summary
It is a universally accepted fact that human can recognize and understand other peoples action from complex natural scene. It attributes the success to hundreds or thousands of neurons in visual cortex of the brain and neural networks formed by their connection in a certain way, which perceive and process motion information of human action for action recognition task. The question is how neurons and neural networks process motion information to perform this task. Researchers have made many neurophysiological studies and obtained some important findings to answer these problems. PLOS ONE | DOI:10.1371/journal.pone.0130569 July 1, 2015
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