Moving Object Tracking based on Kernel and Random-coupled Neural Network
Yiran Chen, Haoran Liu, Mingzhe Liu, Yanhua Liu, Ruili Wang, Peng Li · 2024
Moving object tracking on cost-effective hardware is a crucial need in numerous research and industrial applications. However, current deep learning-based tracking algorithms usually prioritize exceptional performance at the expense of increased computational load. Due to the unavailability of expensive GPUs for many tracking tasks, these popular trackers often fall short in providing robust tracking capabilities with affordable computational resources. This study introduces RCNNshift, a kernel-based tracker that relies on feature extraction from a random-coupled neural network. This visual cortex inspired neural model can extract image features without requiring cumbersome pre-training or deep neural connections. By utilizing an enhanced one-dimensional feature representation, RCNNshift demonstrates superior performance compared to other kernel-based object tracking methods, even those employing higher-dimensional feature spaces. Its improvement in precision and success plots of OPE, compared to the Meanshift and Camshift in the HSV and RGB color spaces, exceeds over 160% and 190% respectively. Comparative experiments have validated the robustness of RCNNshift, showcasing its superior performance over various kernel-based and particle filter trackers. Its combination of robustness and computational efficiency makes RCNNshift an ideal choice for mid to low-end object tracking tasks such as surveillance and underwater tracking. The source code is available at https://github.com/HaoranLiu507/RCNNshift.