Autonomous robot person-following (RPF) systems are crucial for personal assistance and security but suffer from target loss due to occlusions in dynamic, unknown environments. Current methods rely on prebuilt maps and assume static environments, limiting their effectiveness in real-world settings. There is a critical gap in refinding targets under topographic (e.g., walls, corners) and dynamic (e.g., moving pedestrians) occlusions. In this article, we propose a novel heuristic-guided search framework that dynamically builds environmental maps while following the target, and explicitly addresses these two types of occlusions through distinct mechanisms. For topographic occlusions, a belief-guided search field estimates the likelihood of the target's presence and guides search toward promising frontiers. For dynamic occlusions, an observation-based search strategy adaptively switches between a fluid-following field and an overtaking potential field based on occluder motion patterns. Our results demonstrate that the proposed method outperforms existing approaches in terms of search efficiency and success rates, both in simulations and real-world tests. Our target search method enhances the adaptability and reliability of RPF systems in unknown and dynamic environments, supporting their use in real-world applications.