# DAL -- A Deep Depth-aware Long-term Tracker

arXiv: https://arxiv.org/abs/1912.00660 · HTML: https://mlsanity.com/paper/1912.00660/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 26 of 26)

- [Visual Prompt Multi-Modal Tracking](https://mlsanity.com/paper/2303.10826/) (2023)
- [DepthTrack : Unveiling the Power of RGBD Tracking](https://mlsanity.com/paper/2108.13962/) (2021)
- [Prompting for Multi-Modal Tracking](https://mlsanity.com/paper/2207.14571/) (2022)
- [SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking](https://mlsanity.com/paper/2403.16002/) (2024)
- [RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object Tracking](https://mlsanity.com/paper/2208.09787/) (2022)
- [Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking](https://mlsanity.com/paper/2304.14394/) (2023)
- [What You Have is What You Track: Adaptive and Robust Multimodal Tracking](https://mlsanity.com/paper/2507.05899/) (2025)
- [ARKitTrack: A New Diverse Dataset for Tracking Using Mobile RGB-D Data](https://mlsanity.com/paper/2303.13885/) (2023)
- [RGBD Object Tracking: An In-depth Review](https://mlsanity.com/paper/2203.14134/) (2022)
- [CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features](https://mlsanity.com/paper/2505.19434/) (2025)
- [X-Prompt: Multi-modal Visual Prompt for Video Object Segmentation](https://mlsanity.com/paper/2409.19342/) (2024)
- [Visual Object Tracking on Multi-modal RGB-D Videos: A Review](https://mlsanity.com/paper/2201.09207/) (2022)
- [Learning Dual-Fused Modality-Aware Representations for RGBD Tracking](https://mlsanity.com/paper/2211.03055/) (2022)
- [Collaborating Vision, Depth, and Thermal Signals for Multi-Modal Tracking: Dataset and Algorithm](https://mlsanity.com/paper/2509.24741/) (2025)
- [SEATrack: Simple, Efficient, and Adaptive Multimodal Tracker](https://mlsanity.com/paper/2604.12502/) (2026)
- [From Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation](https://mlsanity.com/paper/2403.16834/) (2024)
- [Omni Survey for Multimodality Analysis in Visual Object Tracking](https://mlsanity.com/paper/2508.13000/) (2025)
- [UETrack: A Unified and Efficient Framework for Single Object Tracking](https://mlsanity.com/paper/2603.01412/) (2026)
- [LightFC-X: Lightweight Convolutional Tracker for RGB-X Tracking](https://mlsanity.com/paper/2502.18143/) (2025)
- [UASTrack: A Unified Adaptive Selection Framework with Modality-Customization in Single Object Tracking](https://mlsanity.com/paper/2502.18220/) (2025)
- [RGB-D Video Object Segmentation via Enhanced Multi-store Feature Memory](https://mlsanity.com/paper/2504.16471/) (2025)
- [RGB-D Tracking via Hierarchical Modality Aggregation and Distribution Network](https://mlsanity.com/paper/2504.17595/) (2025)
- [Enhanced Object Tracking by Self-Supervised Auxiliary Depth Estimation Learning](https://mlsanity.com/paper/2405.14195/) (2024)
- [Optimizing Multi-Modality Trackers via Significance-Regularized Tuning](https://mlsanity.com/paper/2508.17488/) (2025)
- [Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning](https://mlsanity.com/paper/2608.01488/) (2026)
- [UniMergeTrack: Hierarchical Model Merging for General RGB-X Visual Object Tracking](https://mlsanity.com/paper/2610.13053/) (2026)

## Abstract (arXiv)

The best RGBD trackers provide high accuracy but are slow to run. On the other hand, the best RGB trackers are fast but clearly inferior on the RGBD datasets. In this work, we propose a deep depth-aware long-term tracker that achieves state-of-the-art RGBD tracking performance and is fast to run. We reformulate deep discriminative correlation filter (DCF) to embed the depth information into deep features. Moreover, the same depth-aware correlation filter is used for target re-detection. Comprehensive evaluations show that the proposed tracker achieves state-of-the-art performance on the Princeton RGBD, STC, and the newly-released CDTB benchmarks and runs 20 fps.