Achieving a balance between accuracy and efficiency is a critical challenge in facial landmark detection (FLD). This paper introduces Parallel Optimal Position Search (POPoS), a high-precision encoding-decoding framework designed to address the limitations of traditional FLD methods. POPoS employs three key contributions: (1) Pseudo-range multilateration is utilized to correct heatmap errors, improving landmark localization accuracy. By integrating multiple anchor points, it reduces the impact of individual heatmap inaccuracies, leading to robust overall positioning. (2) To enhance the pseudo-range accuracy of selected anchor points, a new loss function, named multilateration anchor loss, is proposed. This loss function enhances the accuracy of the distance map, mitigates the risk of local optima, and ensures optimal solutions. (3) A single-step parallel computation algorithm is introduced, boosting computational efficiency and reducing processing time. Extensive evaluations across five benchmark datasets demonstrate that POPoS consistently outperforms existing methods, particularly excelling in low-resolution heatmaps scenarios with minimal computational overhead. These advantages make POPoS as a highly efficient and accurate tool for FLD, with broad applicability in real-world scenarios.

Person in Uniforms Re-identification (PU-ReID) is an emerging computer vision task for various intelligent video surveillance applications. PU-ReID is much understudied due to the absence of large-scale annotated datasets, also this task is extremely challenging because many individuals captured in surveillance videos wear same clothing, introducing significant interference for retrieval tasks owing to the high visual similarity of outfits and subtle differences among individuals. This research initiates the exploration of person in uniforms re-identification, a novel and challenging task tailored for real industrial scenarios. To address these issues, a novel framework is proposed for PU-ReID, which aims to reduce the visual impact of similar uniforms and learn the unique cues derived from human parts and detailed visual features. Specifically, several novel techniques are built in this study: first, a uniform feature separation method with orthogonal constraints is proposed to extract non-uniform features. Second, multi-view subspace feature alignment is introduced to integrate soft-biometrics including optics-related visual features, contextual information of human parts, and cloth-invariant biometric features. In addition, to close the gap between academic research and real-world settings, a new person in uniforms ReID dataset named PU-151 is constructed, which consists of 151 gas station employees in uniforms from 1,488 videos. At last, extensive experiments conducted on five datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art methods. This advancement can drive further developments in re-identification and person search technologies.