# Tracking Reflected Objects: A Benchmark

arXiv: https://arxiv.org/abs/2407.05235 · HTML: https://mlsanity.com/paper/2407.05235/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 108 references that are in the map.
- First posted on arXiv 7 Jul 2024.

## Builds on (most cited first, 30 of 44)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild](https://mlsanity.com/paper/1803.10794/) (2018)
- [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [Probabilistic Regression for Visual Tracking](https://mlsanity.com/paper/2003.12565/) (2020)
- [SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines](https://mlsanity.com/paper/1911.06188/) (2019)
- [SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking](https://mlsanity.com/paper/1911.07241/) (2019)
- [Need for Speed: A Benchmark for Higher Frame Rate Object Tracking](https://mlsanity.com/paper/1703.05884/) (2017)
- [AiATrack: Attention in Attention for Transformer Visual Tracking](https://mlsanity.com/paper/2207.09603/) (2022)
- [Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking](https://mlsanity.com/paper/2203.05328/) (2022)
- [A Novel Performance Evaluation Methodology for Single-Target Trackers](https://mlsanity.com/paper/1503.01313/) (2015)
- [Generalized Relation Modeling for Transformer Tracking](https://mlsanity.com/paper/2303.16580/) (2023)
- [SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation](https://mlsanity.com/paper/2103.10284/) (2021)
- [Robust Object Modeling for Visual Tracking](https://mlsanity.com/paper/2308.05140/) (2023)
- [Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers](https://mlsanity.com/paper/2403.10574/) (2024)
- [Transformer Tracking with Cyclic Shifting Window Attention](https://mlsanity.com/paper/2205.03806/) (2022)
- [HiFT: Hierarchical Feature Transformer for Aerial Tracking](https://mlsanity.com/paper/2108.00202/) (2021)
- [HIPTrack: Visual Tracking with Historical Prompts](https://mlsanity.com/paper/2311.02072/) (2023)
- [Graph Attention Tracking](https://mlsanity.com/paper/2011.11204/) (2020)
- [TCTrack: Temporal Contexts for Aerial Tracking](https://mlsanity.com/paper/2203.01885/) (2022)
- [Learning Spatially Regularized Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1608.05571/) (2016)
- [Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1803.08679/) (2018)
- [Deep Learning for Visual Tracking: A Comprehensive Survey](https://mlsanity.com/paper/1912.00535/) (2019)

## Built on by (most cited first, 1 of 1)

- [Camouflaged Object Tracking: A Benchmark](https://mlsanity.com/paper/2408.13877/) (2024)

## Abstract (arXiv)

Visual tracking has advanced significantly in recent years, mainly due to the availability of large-scale training datasets. These datasets have enabled the development of numerous algorithms that can track objects with high accuracy and robustness.However, the majority of current research has been directed towards tracking generic objects, with less emphasis on more specialized and challenging scenarios. One such challenging scenario involves tracking reflected objects. Reflections can significantly distort the appearance of objects, creating ambiguous visual cues that complicate the tracking process. This issue is particularly pertinent in applications such as autonomous driving, security, smart homes, and industrial production, where accurately tracking objects reflected in surfaces like mirrors or glass is crucial. To address this gap, we introduce TRO, a benchmark specifically for Tracking Reflected Objects. TRO includes 200 sequences with around 70,000 frames, each carefully annotated with bounding boxes. This dataset aims to encourage the development of new, accurate methods for tracking reflected objects, which present unique challenges not sufficiently covered by existing benchmarks. We evaluated 20 state-of-the-art trackers and found that they struggle with the complexities of reflections. To provide a stronger baseline, we propose a new tracker, HiP-HaTrack, which uses hierarchical features to improve performance, significantly outperforming existing algorithms. We believe our benchmark, evaluation, and HiP-HaTrack will inspire further research and applications in tracking reflected objects. The TRO and code are available at https://github.com/OpenCodeGithub/HIP-HaTrack.