  In video surveillance applications, person search is a challenging task
consisting in detecting people and extracting features from their silhouette
for re-identification (re-ID) purpose. We propose a new end-to-end model that
jointly computes detection and feature extraction steps through a single deep
Convolutional Neural Network architecture. Sharing feature maps between the two
tasks for jointly describing people commonalities and specificities allows
faster runtime, which is valuable in real-world applications. In addition to
reaching state-of-the-art accuracy, this multi-task model can be sequentially
trained task-by-task, which results in a broader acceptance of input dataset
types. Indeed, we show that aggregating more pedestrian detection datasets
without costly identity annotations makes the shared feature maps more generic,
and improves re-ID precision. Moreover, these boosted shared feature maps
result in re-ID features more robust to a cross-dataset scenario.
