Research On Spiking Neural Network And Its Application In Object Tracking

Although deep neural networks(DNNs)have shown the great power in many scenarios,they can not be employed in systems with limited computing resources due to their heavy energy consumption.As the third generation of neural networks,spiking neural networks(SNNs)best fit biological neuron mechanism.Compared with DNNs,the calculation and energy consumption of SNNs are much smaller.Now SNNs have shown the effectiveness in image recognition and object detection,but there are few researches on more complex computer vision tasks.As one of the most important tasks in computer vision,object tracking has high real-time and accuracy requirements.It is of great significance to explore the application of SNNs in object tracking.In order to optimize the time distribution of the spike train and reduce the time delay in tracking models,a two-status coding scheme is proposed.Now the mainstream of tracking model adopts Siamese structure based on matching mechanism.Therefore Potential Spiking Matching,Temporal Similarity Matching and Hybrid Spiking Similarity Matching are proposed.They fully use time sequence information to accurately calculate the similarity between the template image and the search image.Based on Two-Status Coding mechanism and Hybrid Spiking Similarity Matching method,Spiking-SiamFC is proposed.It is the first fully-convolutional siamese spiking network which can be used for real-time object tracking in complex scenarios.Experimental results on OTB-2013,OTB-2015 and VOT2016 data sets show that Spiking-SiamFC achieves similar accuracy to SiamFC and has a low time delay,which can achieve real-time tracking effect on the neural morphological chip like TrueNoth.