Research on Intelligent Vehicle Visual Recognition Trajectory Based on Improved Template Matching Tracking Algorithm

In the realm of intelligent vehicles, visual recognition trajectory technology stands as a cornerstone for achieving precise navigation and obstacle avoidance. This study introduces an intelligent vehicle visual recognition trajectory method based on an improved template matching tracking algorithm. It begins by dissecting the limitations of traditional template matching algorithms in dynamic environments, such as sluggish template updating and susceptibility to environmental fluctuations. To address these constraints, the paper proposes an adaptive template updating mechanism, dynamically refreshing the template of the tracked target to enhance tracking stability and accuracy. Moreover, leveraging deep learning techniques optimizes feature extraction and matching processes, bolstering the algorithm's adaptability to complex scenarios. Experimentally, a variety of typical traffic scenes are scrutinized, including highways, urban streets, and intricate intersections. Results underscore the algorithm's superior tracking accuracy and robustness across diverse scenarios, particularly excelling in scenarios marked by lighting variations and occlusions. Furthermore, the algorithm's real-time performance meets practical application demands, effectively supporting intelligent vehicles' visual navigation tasks in diverse environments. Not only does this study refine the visual recognition trajectory technology of intelligent vehicles, but it also furnishes a theoretical underpinning and technical blueprint for the future evolution of intelligent vehicle visual systems. Future endeavors will delve deeper into optimizing the algorithm to accommodate a broader spectrum of traffic environments and confront more intricate real-world challenges.