This chapter investigates the evaluation methodologies and advancements in dynamic visual tracking, focusing on bridging the gap between human cognitive adaptability and machine algorithmic consistency. By integrating neuroscience-inspired human experiments and computer vision benchmarks, a unified evaluation framework is proposed, encompassing 87 sequences and 245,000 frames. Multi-granularity metrics reveal critical insights: humans excel in adaptability within dynamic environments, machines demonstrate consistency in controlled scenarios, and hybrid human-machine systems showcase superior performance by leveraging complementary strengths. The chapter identifies future directions, including decoupling visual capabilities, optimizing scalable evaluation methods, and advancing multimodal algorithms to develop intelligent systems with human-like reasoning and adaptability.