Person Re-identification in the Wild

TL;DR

This paper introduces a new large-scale dataset for person re-identification in videos, evaluates various detection and recognition methods, and proposes improvements to enhance re-identification accuracy.

Contribution

The paper presents the PRW dataset, analyzes the impact of detection on re-identification, and introduces an ID-discriminative embedding and confidence-weighted similarity for better performance.

Findings

Detection improves re-identification accuracy.

CNN-based embeddings enhance identification.

Detection scores can be effectively integrated into similarity measures.

Abstract

We present a novel large-scale dataset and comprehensive baselines for end-to-end pedestrian detection and person recognition in raw video frames. Our baselines address three issues: the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification accuracy and assessing the effectiveness of different detectors for re-identification. We make three distinct contributions. First, a new dataset, PRW, is introduced to evaluate Person Re-identification in the Wild, using videos acquired through six synchronized cameras. It contains 932 identities and 11,816 frames in which pedestrians are annotated with their bounding box positions and identities. Extensive benchmarking results are presented on this dataset. Second, we show that pedestrian detection aids re-identification through two simple yet effective improvements: a discriminatively trained ID-discriminative Embedding (IDE) in the person subspace using convolutional neural network (CNN) features and a Confidence Weighted Similarity (CWS) metric that incorporates detection scores into similarity measurement. Third, we derive insights in evaluating detector performance for the particular scenario of accurate person re-identification.