Re ID Driven Localization Refinement for Person Search
Person search aims at localizing and identifying a query person from a gallery of uncropped scene images. Different from person re identification (re ID) its performance also depends on the localization accuracy of a pedestrian detector. The state of the art methods train the detector individually, and the detected bounding boxes may be sub optimal for the following re ID task. To alleviate this issue, we propose a re ID driven localization refinement framework for providing the refined detection boxes for person search. Specifically, we develop a differentiable ROI transform layer to effectively transform the bounding boxes from the original images. Thus, the box coordinates can be supervised by the re ID training other than the original detection task. With this supervision, the detector can generate more reliable bounding boxes, and the downstream re ID model can produce more discriminative embeddings based on the refined person localizations. Extensive experimental results on the widely used benchmarks demonstrate that our proposed method performs favorably against the state of the art person search methods.
2019 IEEE/CVF International Conference on Computer Vision (ICCV)