Spike-Based High Energy Efficiency and Accuracy Tracker for Robot

Spiking Neural Networks (SNNs) have gained attention for their apparent energy efficiency and significant biological interpretability, although they also face significant challenges such as prolonged latency and suboptimal tracking accuracy. Recent studies have explored the application of SNNs in object tracking tasks. Dynamic visual sensors (DVS) have become a popular way to implement SNN-based object tracking due to their asynchronous and spiking characteristics similar to SNNs. However, challenges such as the high cost of DVS cam- eras and the lack of object surface texture information hinder the utility and performance of DVS trackers. In contrast, RGB information has inherent advantages, including low acquisition cost and comprehensive object surface texture representation. However, RGB information is prone to excessive image blurring in low-light conditions or in fast-motion scenes. To address these challenges, we propose the “Motion Feature Extractor” and the “RGB-DVS Fusion Module”. The “Motion Feature Extractor” can replace the DVS camera at a very low cost, and the “RGB- DVS Fusion Module” can deeply fuse the feature information of the two to make up for their respective deficiencies. In addition, we adopt a conversion method to obtain a lossless SNN version of the model. Through experiments, our model achieves a 13.6% improvement in the expected average overlap (EAO) index using only 1.47% of the energy consumption of SiamRPN (VOT2016 dataset). In addition, we deployed the model to a robot and then conducted tracking experiments, which confirmed that the model can operate on the robot losslessly with satisfactory results.