# Semi-automatic Data Annotation System for Multi-Target Multi-Camera Vehicle Tracking

arXiv: https://arxiv.org/abs/2209.09606 · HTML: https://mlsanity.com/paper/2209.09606/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 16 of 16)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification](https://mlsanity.com/paper/1903.09254/) (2019)
- [Dissecting Person Re-identification from the Viewpoint of Viewpoint](https://mlsanity.com/paper/1812.02162/) (2018)
- [Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification](https://mlsanity.com/paper/2006.12774/) (2020)
- [The 5th AI City Challenge](https://mlsanity.com/paper/2104.12233/) (2021)
- [Vehicle Re-identification in Aerial Imagery: Dataset and Approach](https://mlsanity.com/paper/1904.01400/) (2019)
- [City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones](https://mlsanity.com/paper/2105.06623/) (2021)
- [Aggregate channel features for multi-view face detection](https://mlsanity.com/paper/1407.4023/) (2014)
- [Multi-shot Pedestrian Re-identification via Sequential Decision Making](https://mlsanity.com/paper/1712.07257/) (2017)
- [Multi-camera Multi-Object Tracking](https://mlsanity.com/paper/1709.07065/) (2017)

## Abstract (arXiv)

Multi-target multi-camera tracking (MTMCT) plays an important role in intelligent video analysis, surveillance video retrieval, and other application scenarios. Nowadays, the deep-learning-based MTMCT has been the mainstream and has achieved fascinating improvements regarding tracking accuracy and efficiency. However, according to our investigation, the lacking of datasets focusing on real-world application scenarios limits the further improvements for current learning-based MTMCT models. Specifically, the learning-based MTMCT models training by common datasets usually cannot achieve satisfactory results in real-world application scenarios. Motivated by this, this paper presents a semi-automatic data annotation system to facilitate the real-world MTMCT dataset establishment. The proposed system first employs a deep-learning-based single-camera trajectory generation method to automatically extract trajectories from surveillance videos. Subsequently, the system provides a recommendation list in the following manual cross-camera trajectory matching process. The recommendation list is generated based on side information, including camera location, timestamp relation, and background scene. In the experimental stage, extensive results further demonstrate the efficiency of the proposed system.