# Multi-tracklet Tracking for Generic Targets with Adaptive Detection Clustering

arXiv: https://arxiv.org/abs/2508.05172 · HTML: https://mlsanity.com/paper/2508.05172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 49 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [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)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [Deep Learning in Video Multi-Object Tracking: A Survey](https://mlsanity.com/paper/1907.12740/) (2019)
- [FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking](https://mlsanity.com/paper/1904.04989/) (2019)
- [GMOT-40: A Benchmark for Generic Multiple Object Tracking](https://mlsanity.com/paper/2011.11858/) (2020)
- [Exploit the Connectivity: Multi-Object Tracking with TrackletNet](https://mlsanity.com/paper/1811.07258/) (2018)
- [Siamese-DETR for Generic Multi-Object Tracking](https://mlsanity.com/paper/2310.17875/) (2023)
- [Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking](https://mlsanity.com/paper/1808.01562/) (2018)
- [Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation](https://mlsanity.com/paper/1511.06654/) (2015)

## Abstract (arXiv)

Tracking specific targets, such as pedestrians and vehicles, has been the focus of recent vision-based multitarget tracking studies. However, in some real-world scenarios, unseen categories often challenge existing methods due to low-confidence detections, weak motion and appearance constraints, and long-term occlusions. To address these issues, this article proposes a tracklet-enhanced tracker called Multi-Tracklet Tracking (MTT) that integrates flexible tracklet generation into a multi-tracklet association framework. This framework first adaptively clusters the detection results according to their short-term spatio-temporal correlation into robust tracklets and then estimates the best tracklet partitions using multiple clues, such as location and appearance over time to mitigate error propagation in long-term association. Finally, extensive experiments on the benchmark for generic multiple object tracking demonstrate the competitiveness of the proposed framework.