# Multi-target multi-camera vehicle tracking using transformer-based camera link model and spatial-temporal information

arXiv: https://arxiv.org/abs/2301.07805 · HTML: https://mlsanity.com/paper/2301.07805/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 18 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification](https://mlsanity.com/paper/1903.09254/) (2019)
- [The 5th AI City Challenge](https://mlsanity.com/paper/2104.12233/) (2021)
- [The 4th AI City Challenge](https://mlsanity.com/paper/2004.14619/) (2020)
- [City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones](https://mlsanity.com/paper/2105.06623/) (2021)
- [Multi-Target Multi-Camera Tracking of Vehicles using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model](https://mlsanity.com/paper/2105.01213/) (2021)
- [Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model](https://mlsanity.com/paper/2008.09785/) (2020)

## Built on by (most cited first, 4 of 4)

- [Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports](https://mlsanity.com/paper/2306.13074/) (2023)
- [Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment](https://mlsanity.com/paper/2304.09471/) (2023)
- [GTA: Global Tracklet Association for Multi-Object Tracking in Sports](https://mlsanity.com/paper/2411.08216/) (2024)
- [Sea You Later: Metadata-Guided Long-Term Re-Identification for UAV-Based Multi-Object Tracking](https://mlsanity.com/paper/2311.03561/) (2023)

## Abstract (arXiv)

Multi-target multi-camera tracking (MTMCT) of vehicles, i.e. tracking vehicles across multiple cameras, is a crucial application for the development of smart city and intelligent traffic system. The main challenges of MTMCT of vehicles include the intra-class variability of the same vehicle and inter-class similarity between different vehicles and how to associate the same vehicle accurately across different cameras under large search space. Previous methods for MTMCT usually use hierarchical clustering of trajectories to conduct cross camera association. However, the search space can be large and does not take spatial and temporal information into consideration. In this paper, we proposed a transformer-based camera link model with spatial and temporal filtering to conduct cross camera tracking. Achieving 73.68% IDF1 on the Nvidia Cityflow V2 dataset test set, showing the effectiveness of our camera link model on multi-target multi-camera tracking.