# Person Re-identification in Videos by Analyzing Spatio-Temporal Tubes

arXiv: https://arxiv.org/abs/1902.04856 · HTML: https://mlsanity.com/paper/1902.04856/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 1 of 1)

- [Camera Alignment and Weighted Contrastive Learning for Domain Adaptation in Video Person ReID](https://mlsanity.com/paper/2211.03626/) (2022)

## Abstract (arXiv)

Typical person re-identification frameworks search for k best matches in a gallery of images that are often collected in varying conditions. The gallery may contain image sequences when re-identification is done on videos. However, such a process is time consuming as re-identification has to be carried out multiple times. In this paper, we extract spatio-temporal sequences of frames (referred to as tubes) of moving persons and apply a multi-stage processing to match a given query tube with a gallery of stored tubes recorded through other cameras. Initially, we apply a binary classifier to remove noisy images from the input query tube. In the next step, we use a key-pose detection-based query minimization. This reduces the length of the query tube by removing redundant frames. Finally, a 3-stage hierarchical re-identification framework is used to rank the output tubes as per the matching scores. Experiments with publicly available video re-identification datasets reveal that our framework is better than state-of-the-art methods. It ranks the tubes with an increased CMC accuracy of 6-8% across multiple datasets. Also, our method significantly reduces the number of false positives. A new video re-identification dataset, named Tube-based Reidentification Video Dataset (TRiViD), has been prepared with an aim to help the re-identification research community