Joint Detection and Identification Feature Learning for Person Search

Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world scenarios where the annotations of pedestrian bounding boxes are unavailable and the target person needs to be searched from a gallery of whole scene images. To close the gap, we propose a new deep learning framework for person search. Instead of breaking it down into two separate tasks---pedestrian detection and person re-identification, we jointly handle both aspects in a single convolutional neural network. An Online Instance Matching (OIM) loss function is proposed to train the network effectively, which is scalable to datasets with numerous identities. To validate our approach, we collect and annotate a large-scale benchmark dataset for person search. It contains 18,184 images, 8,432 identities, and 96,143 pedestrian bounding boxes. Experiments show that our framework outperforms other separate approaches, and the proposed OIM loss function converges much faster and better than the conventional Softmax loss.

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Datasets


Introduced in the Paper:

CUHK-SYSU

Used in the Paper:

Market-1501 CUHK03 DukeMTMC-reID

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Person Re-Identification CUHK03 OIM Loss 45 MAP 72.5 # 7
Rank-1 77.5 # 9
Person Re-Identification DukeMTMC-reID OIM Rank-1 68.1 # 71
mAP 47.4 # 75

Methods