# Focus On Details: Online Multi-object Tracking with Diverse Fine-grained Representation

arXiv: https://arxiv.org/abs/2302.14589 · HTML: https://mlsanity.com/paper/2302.14589/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 61 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 30 of 47)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [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)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018)
- [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019)
- [Quasi-Dense Similarity Learning for Multiple Object Tracking](https://mlsanity.com/paper/2006.06664/) (2020)
- [DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion](https://mlsanity.com/paper/2111.14690/) (2021)
- [CityPersons: A Diverse Dataset for Pedestrian Detection](https://mlsanity.com/paper/1702.05693/) (2017)
- [Track to Detect and Segment: An Online Multi-Object Tracker](https://mlsanity.com/paper/2103.08808/) (2021)

## Built on by (most cited first, 25 of 25)

- [Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking](https://mlsanity.com/paper/2308.00783/) (2023)
- [Towards Generalizable Multi-Object Tracking](https://mlsanity.com/paper/2406.00429/) (2024)
- [SAM2MOT: A Novel Paradigm of Multi-Object Tracking by Segmentation](https://mlsanity.com/paper/2504.04519/) (2025)
- [Self-Supervised Multi-Object Tracking with Path Consistency](https://mlsanity.com/paper/2404.05136/) (2024)
- [DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions](https://mlsanity.com/paper/2309.04682/) (2023)
- [DeconfuseTrack:Dealing with Confusion for Multi-Object Tracking](https://mlsanity.com/paper/2403.02767/) (2024)
- [Localization-Guided Track: A Deep Association Multi-Object Tracking Framework Based on Localization Confidence of Detections](https://mlsanity.com/paper/2309.09765/) (2023)
- [Single-Shot and Multi-Shot Feature Learning for Multi-Object Tracking](https://mlsanity.com/paper/2311.10382/) (2023)
- [Associate Everything Detected: Facilitating Tracking-by-Detection to the Unknown](https://mlsanity.com/paper/2409.09293/) (2024)
- [CAMELTrack: Context-Aware Multi-cue ExpLoitation for Online Multi-Object Tracking](https://mlsanity.com/paper/2505.01257/) (2025)
- [NOOUGAT: Towards Unified Online and Offline Multi-Object Tracking](https://mlsanity.com/paper/2509.02111/) (2025)
- [The detection and rectification for identity-switch based on unfalsified control](https://mlsanity.com/paper/2307.14591/) (2023)
- [Multi-Granularity Language-Guided Training for Multi-Object Tracking](https://mlsanity.com/paper/2406.04844/) (2024)
- [BASE: Probably a Better Approach to Multi-Object Tracking](https://mlsanity.com/paper/2309.12035/) (2023)
- [LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition](https://mlsanity.com/paper/2403.04066/) (2024)
- [FusionSORT: Fusion Methods for Online Multi-object Visual Tracking](https://mlsanity.com/paper/2501.00843/) (2025)
- [Occlusion-Aware SORT: Observing Occlusion for Robust Multi-Object Tracking](https://mlsanity.com/paper/2603.06034/) (2026)
- [RTAT: A Robust Two-stage Association Tracker for Multi-Object Tracking](https://mlsanity.com/paper/2408.07344/) (2024)
- [Tracking by Detection and Query: An Efficient End-to-End Framework for Multi-Object Tracking](https://mlsanity.com/paper/2411.06197/) (2024)
- [Deep LG-Track: An Enhanced Localization-Confidence-Guided Multi-Object Tracker](https://mlsanity.com/paper/2504.01457/) (2025)
- [SocialTrack: Multi-Object Tracking in Complex Urban Traffic Scenes Inspired by Social Behavior](https://mlsanity.com/paper/2508.12777/) (2025)
- [StableTrack: Stabilizing Multi-Object Tracking on Low-Frequency Detections](https://mlsanity.com/paper/2511.20418/) (2025)
- [SAMOFT: Robust Multi-Object Tracking via Region and Flow](https://mlsanity.com/paper/2605.09417/) (2026)
- [PS-MOT: Cultivating Instance Awareness from Point Seeds for Multi-Object Tracking](https://mlsanity.com/paper/2606.30476/) (2026)
- [Tracking-by-detection in Multi-object Tracking: Survey and Experiments](https://mlsanity.com/paper/2609.08265/) (2026)

## Abstract (arXiv)

Discriminative representation is essential to keep a unique identifier for each target in Multiple object tracking (MOT). Some recent MOT methods extract features of the bounding box region or the center point as identity embeddings. However, when targets are occluded, these coarse-grained global representations become unreliable. To this end, we propose exploring diverse fine-grained representation, which describes appearance comprehensively from global and local perspectives. This fine-grained representation requires high feature resolution and precise semantic information. To effectively alleviate the semantic misalignment caused by indiscriminate contextual information aggregation, Flow Alignment FPN (FAFPN) is proposed for multi-scale feature alignment aggregation. It generates semantic flow among feature maps from different resolutions to transform their pixel positions. Furthermore, we present a Multi-head Part Mask Generator (MPMG) to extract fine-grained representation based on the aligned feature maps. Multiple parallel branches of MPMG allow it to focus on different parts of targets to generate local masks without label supervision. The diverse details in target masks facilitate fine-grained representation. Eventually, benefiting from a Shuffle-Group Sampling (SGS) training strategy with positive and negative samples balanced, we achieve state-of-the-art performance on MOT17 and MOT20 test sets. Even on DanceTrack, where the appearance of targets is extremely similar, our method significantly outperforms ByteTrack by 5.0% on HOTA and 5.6% on IDF1. Extensive experiments have proved that diverse fine-grained representation makes Re-ID great again in MOT.