This paper presents a dual mmWave radar system with our sequential clustering method to address two significant challenges in radar-based human detection. The first challenge is that conventional clustering algorithms are highly dependent on the predefined parameters such as the number of clusters and epsilon, and are susceptible to environmental noise. The second is the performance degradation of many detection methods in real-world environments, primarily due to the sparse data from single-chip radar systems. Based on our dual-radar data, the proposed algorithm first employs density-based clustering to sequentially identify potential targets while effectively filtering noise. Subsequently, a border-peeling technique is introduced to confirm merged clusters, separating individuals in dense crowds. Finally, the refined parameters of potential targets are used to initialize a Gaussian Mixture Model (GMM), which generates the final, accurate clustering results. Results demonstrate that our clustering method is effective for public datasets and real-world environments, like offline marketing and social distancing monitoring for smart space area management.