# Unveiling the Power of Deep Tracking

arXiv: https://arxiv.org/abs/1804.06833 · HTML: https://mlsanity.com/paper/1804.06833/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 30 of 41)

- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking](https://mlsanity.com/paper/2103.11681/) (2021)
- [Learning Target Candidate Association to Keep Track of What Not to Track](https://mlsanity.com/paper/2103.16556/) (2021)
- [Transforming Model Prediction for Tracking](https://mlsanity.com/paper/2203.11192/) (2022)
- [STMTrack: Template-free Visual Tracking with Space-time Memory Networks](https://mlsanity.com/paper/2104.00324/) (2021)
- [Robust Object Modeling for Visual Tracking](https://mlsanity.com/paper/2308.05140/) (2023)
- [Transformer Tracking with Cyclic Shifting Window Attention](https://mlsanity.com/paper/2205.03806/) (2022)
- [Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds](https://mlsanity.com/paper/2108.04728/) (2021)
- [Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook](https://mlsanity.com/paper/2112.02838/) (2021)
- [Active Learning for Deep Visual Tracking](https://mlsanity.com/paper/2110.13259/) (2021)
- [Dense Gaussian Processes for Few-Shot Segmentation](https://mlsanity.com/paper/2110.03674/) (2021)
- [Learning to Filter: Siamese Relation Network for Robust Tracking](https://mlsanity.com/paper/2104.00829/) (2021)
- [Learning Tracking Representations via Dual-Branch Fully Transformer Networks](https://mlsanity.com/paper/2112.02571/) (2021)
- [TrTr: Visual Tracking with Transformer](https://mlsanity.com/paper/2105.03817/) (2021)
- [SiamMask: A Framework for Fast Online Object Tracking and Segmentation](https://mlsanity.com/paper/2207.02088/) (2022)
- [Beyond Visual Cues: Synchronously Exploring Target-Centric Semantics for Vision-Language Tracking](https://mlsanity.com/paper/2311.17085/) (2023)
- [Improving Visual Object Tracking through Visual Prompting](https://mlsanity.com/paper/2409.18901/) (2024)
- [Target Transformed Regression for Accurate Tracking](https://mlsanity.com/paper/2104.00403/) (2021)
- [SiamCorners: Siamese Corner Networks for Visual Tracking](https://mlsanity.com/paper/2104.07303/) (2021)
- [Cognitive Disentanglement for Referring Multi-Object Tracking](https://mlsanity.com/paper/2503.11496/) (2025)
- [A Discriminative Single-Shot Segmentation Network for Visual Object Tracking](https://mlsanity.com/paper/2112.11846/) (2021)
- [GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences](https://mlsanity.com/paper/2207.07629/) (2022)
- [Trans2k: Unlocking the Power of Deep Models for Transparent Object Tracking](https://mlsanity.com/paper/2210.03436/) (2022)
- [A New Dataset and a Distractor-Aware Architecture for Transparent Object Tracking](https://mlsanity.com/paper/2401.03872/) (2024)
- [Benchmarking Deep Trackers on Aerial Videos](https://mlsanity.com/paper/2103.12924/) (2021)
- [Leveraging the Power of Data Augmentation for Transformer-based Tracking](https://mlsanity.com/paper/2309.08264/) (2023)
- [Real-Time Visual Object Tracking via Few-Shot Learning](https://mlsanity.com/paper/2103.10130/) (2021)
- [Context-aware Visual Tracking with Joint Meta-updating](https://mlsanity.com/paper/2204.01513/) (2022)
- [Event Stream-based Visual Object Tracking: HDETrack V2 and A High-Definition Benchmark](https://mlsanity.com/paper/2502.05574/) (2025)

## Abstract (arXiv)

In the field of generic object tracking numerous attempts have been made to exploit deep features. Despite all expectations, deep trackers are yet to reach an outstanding level of performance compared to methods solely based on handcrafted features. In this paper, we investigate this key issue and propose an approach to unlock the true potential of deep features for tracking. We systematically study the characteristics of both deep and shallow features, and their relation to tracking accuracy and robustness. We identify the limited data and low spatial resolution as the main challenges, and propose strategies to counter these issues when integrating deep features for tracking. Furthermore, we propose a novel adaptive fusion approach that leverages the complementary properties of deep and shallow features to improve both robustness and accuracy. Extensive experiments are performed on four challenging datasets. On VOT2017, our approach significantly outperforms the top performing tracker from the challenge with a relative gain of 17% in EAO.