# MotionDLO: Hybrid Event- and Frame-Based Tracking of Deformable Linear Objects

arXiv: https://arxiv.org/abs/2608.22398 · HTML: https://mlsanity.com/paper/2608.22398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 59 references that are in the map.
- First posted on arXiv 23 Aug 2026.

## Builds on (most cited first, 30 of 32)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [Instances as Queries](https://mlsanity.com/paper/2105.01928/) (2021)
- [E-RAFT: Dense Optical Flow from Event Cameras](https://mlsanity.com/paper/2108.10552/) (2021)
- [VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://mlsanity.com/paper/2108.05015/) (2021)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [YOLACT++: Better Real-time Instance Segmentation](https://mlsanity.com/paper/1912.06218/) (2019)
- [Multi-Stage Cable Routing through Hierarchical Imitation Learning](https://mlsanity.com/paper/2307.08927/) (2023)
- [Asynchronous Blob Tracker for Event Cameras](https://mlsanity.com/paper/2307.10593/) (2023)
- [Occlusion-robust Deformable Object Tracking without Physics Simulation](https://mlsanity.com/paper/2101.00733/) (2021)
- [Data-Driven Feature Tracking for Event Cameras With and Without Frames](https://mlsanity.com/paper/2211.12826/) (2022)
- [Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation](https://mlsanity.com/paper/2312.01236/) (2023)
- [Event Stream based Human Action Recognition: A High-Definition Benchmark Dataset and Algorithms](https://mlsanity.com/paper/2408.09764/) (2024)
- [Fast Event-based Optical Flow Estimation by Triplet Matching](https://mlsanity.com/paper/2212.12218/) (2022)
- [Powerline Tracking with Event Cameras](https://mlsanity.com/paper/2108.00515/) (2021)
- [Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction](https://mlsanity.com/paper/2302.03793/) (2023)
- [mBEST: Realtime Deformable Linear Object Detection Through Minimal Bending Energy Skeleton Pixel Traversals](https://mlsanity.com/paper/2302.09444/) (2023)
- [Fully Spiking Neural Networks for Unified Frame-Event Object Tracking](https://mlsanity.com/paper/2505.20834/) (2025)
- [ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models](https://mlsanity.com/paper/2502.03266/) (2025)
- [Let's take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation](https://mlsanity.com/paper/1810.04461/) (2018)
- [MultiDLO: Simultaneous Shape Tracking of Multiple Deformable Linear Objects with Global-Local Topology Preservation](https://mlsanity.com/paper/2310.13245/) (2023)
- [Segment Any Events with Language](https://mlsanity.com/paper/2601.23159/) (2026)
- [ISCUTE: Instance Segmentation of Cables Using Text Embedding](https://mlsanity.com/paper/2402.11996/) (2024)

## Abstract (arXiv)

Reliably tracking moving deformable linear objects (DLOs) while simultaneously ensuring robustness, accuracy, and temporally consistent state estimation remains a fundamental challenge in robot perception. We introduce MotionDLO, a real-time tracking framework specifically designed to overcome these limitations in temporal continuity and latency. The method exploits the high temporal resolution and sparsity of event-based cameras and combines segmentation with the Coherent Point Drift (CPD) algorithm under the principles of Motion Coherence Theory. This integration enables temporally consistent shape estimation while maintaining a low computational overhead. Existing event-based tracking methods are typically computationally efficient but exhibit reduced accuracy compared to frame-based approaches, or alternatively compromise event sparsity to achieve competitive performance. To resolve this trade-off, we propose a hybrid event- and frame-based tracking architecture that preserves the complementary strengths of both sensing modalities. The event stream ensures high-frequency motion updates, while frame-based information stabilizes spatial accuracy and object identity. We demonstrate that the proposed framework reliably associates DLO instances across video sequences, enabling robust perception for robotic manipulation tasks. Experimental results validate real-time performance at 12 ms update rates and accurate shape tracking with an point-to-curve error as measurement of accuracy of up to 0.43 mm, supporting dynamic path adaptation during manipulation. The source code and demonstration datasets are publicly available.