RCNNshift: Moving Object Tracking with Kernel and Training-Free Spiking Neural Network

Moving object tracking is essential in computer vision applications such as autonomous navigation and surveillance. Traditional kernel-based methods like Meanshift and Camshift are computationally efficient but often falter with complex motions and occlusions. Deep learning approaches improve performance but require significant computational resources and extensive pretraining. In this paper, we present RCNNshift, a novel tracking algorithm that integrates Three-Dimensional Random-Coupled Neural Networks (3DRCNN) with the kernel-based window update process, eliminating the need for pretraining. RCNNshift constructs a robust feature space by leveraging spatiotemporal information from 3DRCNN, enabling effective tracking with reduced computational overhead. Additionally, it employs a one-dimensional feature space for the kernel-based window update step, which can improve efficiency compared to conventional multi-dimensional approaches. We evaluated RCNNshift on multiple datasets, where it consistently outperformed Meanshift and Camshift in accuracy and reliability, particularly in high-resolution and stable remote sensing environments. RCNNshift offers a promising balance between performance and computational efficiency, making it suitable for real-world applications where resources are limited.