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Spiking two-stream methods with unsupervised STDP-based learning for action recognition
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DOI：10.1016/j.image.2025.117263.png)
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Abstract
En 中文
Video analysis is a computer vision task that is useful for many applications like surveillance, human-machine interaction, and autonomous vehicles. Deep learning methods are currently the state-of-the-art methods for video analysis. Particularly, two-stream methods, which leverage both spatial and temporal information, have proven to be valuable in Human Action Recognition (HAR). However, they have high computational costs, and need a large amount of labeled data for training. In addressing these challenges, this paper adopts amore efficient approach by leveraging Convolutional Spiking Neural Networks (CSNNs) trained with the unsupervised Spike Timing-Dependent Plasticity (STDP) learning rule for action classification. These networks represent the information using asynchronous low-energy spikes, which allows the network to be more energy efficient when implemented on neuromorphic hardware. Furthermore, learning visual features with unsupervised learning reduces the need for labeled data during training, making the approach doubly advantageous. Therefore, we explore transposing two-stream convolutional neural networks into the spiking domain, where we train each stream with the unsupervised STDP learning rule. We investigate the performance of these networks in video analysis by employing five distinct configurations for the temporal stream, and evaluate them across four benchmark HAR datasets. In this work, we show that two-stream CSNNs can successfully extract spatio-temporal information from videos despite using limited training data, and that the spiking spatial and temporal streams are complementary. We also show that replacing a dedicated temporal stream with a spatio-temporal one within a spiking two-stream architecture leads to information redundancy that hinders the performance.
Keywords:
Spiking neural networks
STDP
Action classification
Two-stream architecture
Spatial and temporal features
3D convolution
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