# Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker arXiv: https://arxiv.org/abs/2205.00968 ยท HTML: https://mlsanity.com/paper/2205.00968/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 53 of its 70 references that are in the map. - First posted on arXiv 2 May 2022. ## Builds on (most cited first, 30 of 53) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [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) - [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016) - [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017) - [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020) - [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018) - [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019) - [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015) - [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016) - [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017) - [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018) - [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021) - [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018) - [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017) - [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016) - [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) - [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017) - [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016) - [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019) - [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) - [Towards Open World Object Detection](https://mlsanity.com/paper/2103.02603/) (2021) - [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018) ## Built on by (most cited first, 15 of 15) - [QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking](https://mlsanity.com/paper/2210.06984/) (2022) - [LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching](https://mlsanity.com/paper/2306.11925/) (2023) - [A Generalized Framework for Video Instance Segmentation](https://mlsanity.com/paper/2211.08834/) (2022) - [RobMOT: Robust 3D Multi-Object Tracking by Observational Noise and State Estimation Drift Mitigation on LiDAR PointCloud](https://mlsanity.com/paper/2405.11536/) (2024) - [FeatureSORT: Essential Features for Effective Tracking](https://mlsanity.com/paper/2407.04249/) (2024) - [ExGra-Med: Extended Context Graph Alignment for Medical Vision-Language Models](https://mlsanity.com/paper/2410.02615/) (2024) - [Localization-Guided Track: A Deep Association Multi-Object Tracking Framework Based on Localization Confidence of Detections](https://mlsanity.com/paper/2309.09765/) (2023) - [FastTracker: Real-Time and Accurate Visual Tracking](https://mlsanity.com/paper/2508.14370/) (2025) - [X Modality Assisting RGBT Object Tracking](https://mlsanity.com/paper/2312.17273/) (2023) - [SFSORT: Scene Features-based Simple Online Real-Time Tracker](https://mlsanity.com/paper/2404.07553/) (2024) - [MAML MOT: Multiple Object Tracking based on Meta-Learning](https://mlsanity.com/paper/2405.07272/) (2024) - [S3Track: Self-supervised Tracking with Soft Assignment Flow](https://mlsanity.com/paper/2305.09981/) (2023) - [ParGANDA: Making Synthetic Pedestrians A Reality For Object Detection](https://mlsanity.com/paper/2307.11360/) (2023) - [RTAT: A Robust Two-stage Association Tracker for Multi-Object Tracking](https://mlsanity.com/paper/2408.07344/) (2024) - [GRACE: Geometry- and Ray-Aware Camera-Efficient Multi-View Pedestrian Tracking](https://mlsanity.com/paper/2609.16872/) (2026) ## Abstract (arXiv) In existing joint detection and tracking methods, pairwise relational features are used to match previous tracklets to current detections. However, the features may not be discriminative enough for a tracker to identify a target from a large number of detections. Selecting only high-scored detections for tracking may lead to missed detections whose confidence score is low. Consequently, in the online setting, this results in disconnections of tracklets which cannot be recovered. In this regard, we present Sparse Graph Tracker (SGT), a novel online graph tracker using higher-order relational features which are more discriminative by aggregating the features of neighboring detections and their relations. SGT converts video data into a graph where detections, their connections, and the relational features of two connected nodes are represented by nodes, edges, and edge features, respectively. The strong edge features allow SGT to track targets with tracking candidates selected by top-K scored detections with large K. As a result, even low-scored detections can be tracked, and the missed detections are also recovered. The robustness of K value is shown through the extensive experiments. In the MOT16/17/20 and HiEve Challenge, SGT outperforms the state-of-the-art trackers with real-time inference speed. Especially, a large improvement in MOTA is shown in the MOT20 and HiEve Challenge. Code is available at https://github.com/HYUNJS/SGT.