Norm-Aware Embedding for Efficient Person Search and Tracking

Person detection and Re-identification are two well-defined support tasks for practically relevant tasks such as Person Search and Multiple Person Tracking. Person Search aims to find and locate all instances with the same identity as the query person in a set of panoramic gallery images. Similarly, Multiple Person Tracking, especially when using the tracking-by-detection pipeline, requires to detect and associate all appeared persons in consecutive video frames. One major challenge shared by the two tasks comes from the contradictory goals of detection and re-identification, i.e, person detection focuses on finding the commonness of all persons while person re-ID handles the differences among multiple identities. Therefore, it is crucial to reconcile the relationship between the two support tasks in a joint model. To this end, we present a novel approach called Norm-Aware Embedding to disentangle the person embedding into norm and angle for detection and re-ID respectively, allowing for both effective and efficient multi-task training. We further extend the proposal-level person embedding to pixel-level, whose discrimination ability is less affected by misalignment. Our Norm-Aware Embedding achieves remarkable performance on both person search and multiple person tracking benchmarks, with the merit of being easy to train and resource-friendly.