# TrADe Re-ID -- Live Person Re-Identification using Tracking and Anomaly Detection

arXiv: https://arxiv.org/abs/2209.06452 · HTML: https://mlsanity.com/paper/2209.06452/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 14 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Person Re-identification: Past, Present and Future](https://mlsanity.com/paper/1610.02984/) (2016)
- [Learning Deep Features for One-Class Classification](https://mlsanity.com/paper/1801.05365/) (2018)
- [Survey on Reliable Deep Learning-Based Person Re-Identification Models: Are We There Yet?](https://mlsanity.com/paper/2005.00355/) (2020)
- [Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering](https://mlsanity.com/paper/2009.14178/) (2020)
- [Towards Practical Implementations of Person Re-Identification from Full Video Frames](https://mlsanity.com/paper/2009.01377/) (2020)

## Built on by (most cited first, 2 of 2)

- [Benchmarking person re-identification datasets and approaches for practical real-world implementations](https://mlsanity.com/paper/2212.09981/) (2022)
- [A Multilevel Strategy to Improve People Tracking in a Real-World Scenario](https://mlsanity.com/paper/2404.18876/) (2024)

## Abstract (arXiv)

Person Re-Identification (Re-ID) aims to search for a person of interest (query) in a network of cameras. In the classic Re-ID setting the query is sought in a gallery containing properly cropped images of entire bodies. Recently, the live Re-ID setting was introduced to represent the practical application context of Re-ID better. It consists in searching for the query in short videos, containing whole scene frames. The initial live Re-ID baseline used a pedestrian detector to build a large search gallery and a classic Re-ID model to find the query in the gallery. However, the galleries generated were too large and contained low-quality images, which decreased the live Re-ID performance. Here, we present a new live Re-ID approach called TrADe, to generate lower high-quality galleries. TrADe first uses a Tracking algorithm to identify sequences of images of the same individual in the gallery. Following, an Anomaly Detection model is used to select a single good representative of each tracklet. TrADe is validated on the live Re-ID version of the PRID-2011 dataset and shows significant improvements over the baseline.