# Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking

arXiv: https://arxiv.org/abs/2002.05583 · HTML: https://mlsanity.com/paper/2002.05583/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

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

- [VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://mlsanity.com/paper/2108.05015/) (2021)
- [Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline](https://mlsanity.com/paper/2309.14611/) (2023)
- [Prompting for Multi-Modal Tracking](https://mlsanity.com/paper/2207.14571/) (2022)
- [Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric](https://mlsanity.com/paper/2211.11010/) (2022)
- [Frame-Event Alignment and Fusion Network for High Frame Rate Tracking](https://mlsanity.com/paper/2305.15688/) (2023)
- [CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras](https://mlsanity.com/paper/2401.02826/) (2024)
- [Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation](https://mlsanity.com/paper/2303.09919/) (2023)
- [Asynchronous Blob Tracker for Event Cameras](https://mlsanity.com/paper/2307.10593/) (2023)
- [Data-Driven Feature Tracking for Event Cameras With and Without Frames](https://mlsanity.com/paper/2211.12826/) (2022)
- [Table tennis ball spin estimation with an event camera](https://mlsanity.com/paper/2404.09870/) (2024)
- [Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking](https://mlsanity.com/paper/2108.04521/) (2021)
- [SiamEvent: Event-based Object Tracking via Edge-aware Similarity Learning with Siamese Networks](https://mlsanity.com/paper/2109.13456/) (2021)
- [Probabilistic Approach for Detection of High-Frequency Periodic Signals using an Event Camera](https://mlsanity.com/paper/2205.04691/) (2022)
- [High-temporal-resolution event-based vehicle detection and tracking](https://mlsanity.com/paper/2212.14289/) (2022)
- [Towards Low-Latency Event Stream-based Visual Object Tracking: A Slow-Fast Approach](https://mlsanity.com/paper/2505.12903/) (2025)
- [ESTR-CoT: Towards Explainable and Accurate Event Stream based Scene Text Recognition with Chain-of-Thought Reasoning](https://mlsanity.com/paper/2507.02200/) (2025)
- [Optical Flow-Guided 6DoF Object Pose Tracking with an Event Camera](https://mlsanity.com/paper/2512.21053/) (2025)
- [On the Theory of Bias Tuning in Event Cameras](https://mlsanity.com/paper/2501.18788/) (2025)
- [Event Stream-based Visual Object Tracking: HDETrack V2 and A High-Definition Benchmark](https://mlsanity.com/paper/2502.05574/) (2025)
- [EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perception](https://mlsanity.com/paper/2504.16616/) (2025)
- [ASUMOT: Motion-Consistency-Based Asynchronous UAV Detection and Tracking with Event Cameras](https://mlsanity.com/paper/2607.11303/) (2026)
- [SE-Harris and eSUSAN: Asynchronous Event-Based Corner Detection Using Megapixel Resolution CeleX-V Camera](https://mlsanity.com/paper/2105.00480/) (2021)
- [Event Data Association via Robust Model Fitting for Event-based Object Tracking](https://mlsanity.com/paper/2110.12962/) (2021)
- [Event Driven Clustering Algorithm](https://mlsanity.com/paper/2602.00115/) (2026)
- [Dynamic Pondering Sparsity-aware Mixture-of-Experts Transformer for Event Stream based Visual Object Tracking](https://mlsanity.com/paper/2605.06112/) (2026)

## Abstract (arXiv)

Event cameras, which are asynchronous bio-inspired vision sensors, have shown great potential in a variety of situations, such as fast motion and low illumination scenes. However, most of the event-based object tracking methods are designed for scenarios with untextured objects and uncluttered backgrounds. There are few event-based object tracking methods that support bounding box-based object tracking. The main idea behind this work is to propose an asynchronous Event-based Tracking-by-Detection (ETD) method for generic bounding box-based object tracking. To achieve this goal, we present an Adaptive Time-Surface with Linear Time Decay (ATSLTD) event-to-frame conversion algorithm, which asynchronously and effectively warps the spatio-temporal information of asynchronous retinal events to a sequence of ATSLTD frames with clear object contours. We feed the sequence of ATSLTD frames to the proposed ETD method to perform accurate and efficient object tracking, which leverages the high temporal resolution property of event cameras. We compare the proposed ETD method with seven popular object tracking methods, that are based on conventional cameras or event cameras, and two variants of ETD. The experimental results show the superiority of the proposed ETD method in handling various challenging environments.