S4-KD: A single step spiking SiamFC+ + for object tracking with knowledge distillation

Spiking neural networks (SNNs), which transmit information through binary spikes, have the advantages of high efficiency and low energy consumption. At present, the multiple time steps of SNNs can lead to increased latency and power consumption. To this end, we propose Single Step Spiking SiamFC+ + (S4), an improved single-step end-to-end direct training target tracking framework that compresses the time step to 1 by temporal pruning, using AlexNet as the backbone network. Experimental results show that, even when only a single time step is used, the tracking performance of the proposed S4 is still comparable to the original Spiking SiamFC+ +. Furthermore, we introduce the knowledge distillation to improve the performance of the proposed S4, which is called S4-KD for clarity. Three kinds of distillation loss functions are designed for the S4-KD. An artificial neural network model based on the AlexNet network serves as the teacher model, while the temporal-pruned S4 model acts as the student model for retraining. Experimental results show that the S4-KD tracker achieves higher performance on several tracking benchmarks. More specifically, on the OTB100 dataset, Precision and Success are 0.871 and 0.657 respectively, on the UAV123 dataset, Precision and Success are 0.766 and 0.603 respectively, and on the VOT2018 dataset, A, R, and EAO are 0.582, 0.370, and 0.278 respectively. In addition, the estimated energy consumption of the S4-KD is only 34.6 % of that of the original Spiking SiamFC+ +. To the best of our knowledge, the proposed S4-KD tracker surpasses all the existing SNN-based object tracking methods, achieving state-of-the-art performance. Our codes will be available at https://github.com/PSNN-xd/S4-KD.