End-to-end Person Search Sequentially Trained on Aggregated Dataset

• URL: http://arxiv.org/abs/2201.09604v1
• Date: Mon, 24 Jan 2022 11:22:15 GMT
• Title: End-to-end Person Search Sequentially Trained on Aggregated Dataset
• Authors: Angelique Loesch and Jaonary Rabarisoa and Romaric Audigier
• Abstract summary: We propose a new end-to-end model that jointly computes detection and feature extraction steps. We show that aggregating more pedestrian detection datasets without costly identity annotations makes the shared feature maps more generic.
• Score: 1.9766522384767227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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