# Cross-Camera Trajectories Help Person Retrieval in a Camera Network

arXiv: https://arxiv.org/abs/2204.12900 · HTML: https://mlsanity.com/paper/2204.12900/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 57 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [Re-ranking Person Re-identification with k-reciprocal Encoding](https://mlsanity.com/paper/1701.08398/) (2017)
- [Harmonious Attention Network for Person Re-Identification](https://mlsanity.com/paper/1802.08122/) (2018)
- [Style Normalization and Restitution for Generalizable Person Re-identification](https://mlsanity.com/paper/2005.11037/) (2020)
- [High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification](https://mlsanity.com/paper/2003.08177/) (2020)
- [Pose-guided Visible Part Matching for Occluded Person ReID](https://mlsanity.com/paper/2004.00230/) (2020)
- [Camera-aware Proxies for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2012.10674/) (2020)
- [VRSTC: Occlusion-Free Video Person Re-Identification](https://mlsanity.com/paper/1907.08427/) (2019)
- [Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification](https://mlsanity.com/paper/1910.10111/) (2019)
- [Spatial-Temporal Person Re-identification](https://mlsanity.com/paper/1812.03282/) (2018)
- [Illumination-Adaptive Person Re-identification](https://mlsanity.com/paper/1905.04525/) (2019)
- [Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification](https://mlsanity.com/paper/1808.01911/) (2018)
- [Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns](https://mlsanity.com/paper/1803.07293/) (2018)
- [Deep Ranking for Person Re-identification via Joint Representation Learning](https://mlsanity.com/paper/1505.06821/) (2015)
- [Top-push Video-based Person Re-identification](https://mlsanity.com/paper/1604.08683/) (2016)
- [Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization](https://mlsanity.com/paper/1910.11560/) (2019)
- [Locality Aware Appearance Metric for Multi-Target Multi-Camera Tracking](https://mlsanity.com/paper/1911.12037/) (2019)

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

- [GRAP-MOT: Unsupervised Graph-based Position Weighted Person Multi-camera Multi-object Tracking in a Highly Congested Space](https://mlsanity.com/paper/2510.21482/) (2025)

## Abstract (arXiv)

We are concerned with retrieving a query person from multiple videos captured by a non-overlapping camera network. Existing methods often rely on purely visual matching or consider temporal constraints but ignore the spatial information of the camera network. To address this issue, we propose a pedestrian retrieval framework based on cross-camera trajectory generation, which integrates both temporal and spatial information. To obtain pedestrian trajectories, we propose a novel cross-camera spatio-temporal model that integrates pedestrians' walking habits and the path layout between cameras to form a joint probability distribution. Such a spatio-temporal model among a camera network can be specified using sparsely sampled pedestrian data. Based on the spatio-temporal model, cross-camera trajectories can be extracted by the conditional random field model and further optimized by restricted non-negative matrix factorization. Finally, a trajectory re-ranking technique is proposed to improve the pedestrian retrieval results. To verify the effectiveness of our method, we construct the first cross-camera pedestrian trajectory dataset, the Person Trajectory Dataset, in real surveillance scenarios. Extensive experiments verify the effectiveness and robustness of the proposed method.