Towards Real-Time Person Search with Invariant Feature Learning

Person search aims to locate a query person in a gallery of unconstrained scene images, which has many real-world applications. However, existing methods directly build off of advances in object detection for better performance rather than efficiency. Complex designs in heavy-weight detectors are redundant for person search. Furthermore, challenges in person search force existing methods to employ additional modules, which greatly deteriorates models' efficiency. In this paper, we propose a novel real-time framework for both effective and efficient person search, termed as InvarPS. InvarPS optimizes the over-designed network with invariant feature learning. Specifically, considering the main challenges (appearance changes, scale variations, and conflicting tasks) in person search, we propose an improved backbone, a Single-Scale Feature Fusion (SSFF) module and a Hierarchical Decoupling Head (HDH) to facilitate the model learning appearance, scale, and task invariant features, respectively. Extensive experiments demonstrate that our method, with only half the complexity of original detectors, achieves state-of-the-art performance with real-time speed (>100 FPS).