A Long-Term Vehicle Tracking Algorithm of Correlation Filter Optimized by Swarm Intelligence Tracking Framework

In response to the complex situations encountered in real traffic scenarios, such as sudden acceleration or deceleration of vehicles, similar-looking vehicles, and occlusions caused by other vehicles or obstacles, which severely affect tracking accuracy, a long-term vehicle tracking algorithm based on correlation filter (CF) is proposed and optimized using the swarm intelligence (SI) tracking framework. The fast-discriminative scale space tracking (FDSST) of CF is adopted as the core tracker. Multiple features are adaptively weighted and fused. The optimal feature template is dynamically updated based on confidence. An in-depth analysis of the intrinsic relationship is conducted between SI and object tracking, leading to the design of an SI tracking framework. Within this framework, the carnivorous plant algorithm (CPA) is employed as an optimization method, further enhancing CPA functionality through phototaxis strategy and population partition mechanism. During FDSST tracking, when tracking uncertainty surpasses a set threshold, the SI tracking framework is integrated to rectify tracking outcomes, and a short-term memory module is designed to predict the object position if the object disappears for dozens of frames. The experimental results on benchmark datasets (UAV20L and LaSOT) demonstrate a success rate (SR) of 67.45%, a precision (PR) rate of 70.50%, and a speed of 22.22 ft/s. Comparative analyses with other notable tracking algorithms confirm the exceptional accuracy and robustness of the proposed approach that can effectively address diverse challenges in vehicle tracking scenarios, by achieving highly reliable tracking outcomes and enhancing long-term vehicle tracking capability.