基于点击驱动的人体解析算法研究

In this paper, a click-driven human parsing algorithm is proposed to enable precise segmentation of specific target instances, addressing issues of segmentation redundancy and high computational complexity in existing methods. A multi-module collaborative framework is introduced, including a center-offset feature fusion module, a spatial instance decoupling module, and a random point instance matching module. The center module extracts multi-scale features through a feature pyramid network and integrates offset features; the decoupling module optimizes instance features via spatial and channel recalibration; and the random point module simulates user clicks for instance matching. Experiments conducted on the MHPv2.0 dataset, using metrics such as APp vol, demonstrate that the proposed algorithm significantly enhances time efficiency and memory utilization in both single-person and multi-person parsing scenarios, exhibiting strong robustness and accuracy in complex multi-person contexts. Compared to traditional methods, the algorithm achieves notable reductions in time consumption and memory usage while delivering superior instance segmentation accuracy. By leveraging module collaboration, the click-driven human parsing algorithm improves parsing efficiency, making it suitable for real-time processing and resource-constrained scenarios. However, further improvements are needed in crowded and occluded situations. Future work could integrate attention mechanisms or image enhancement techniques to enhance performance and extend the algorithm to other application domains.