  We address the task of person search, that is, localizing and re-identifying
query persons from a set of raw scene images. Recent approaches are typically
built upon OIMNet, a pioneer work on person search, that learns joint person
representations for performing both detection and person re-identification
(reID) tasks. To obtain the representations, they extract features from
pedestrian proposals, and then project them on a unit hypersphere with L2
normalization. These methods also incorporate all positive proposals, that
sufficiently overlap with the ground truth, equally to learn person
representations for reID. We have found that 1) the L2 normalization without
considering feature distributions degenerates the discriminative power of
person representations, and 2) positive proposals often also depict background
clutter and person overlaps, which could encode noisy features to person
representations. In this paper, we introduce OIMNet++ that addresses the
aforementioned limitations. To this end, we introduce a novel normalization
layer, dubbed ProtoNorm, that calibrates features from pedestrian proposals,
while considering a long-tail distribution of person IDs, enabling L2
normalized person representations to be discriminative. We also propose a
localization-aware feature learning scheme that encourages better-aligned
proposals to contribute more in learning discriminative representations.
Experimental results and analysis on standard person search benchmarks
demonstrate the effectiveness of OIMNet++.
