# Depth Masked Discriminative Correlation Filter

arXiv: https://arxiv.org/abs/1802.09227 · HTML: https://mlsanity.com/paper/1802.09227/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 6 of 6)

- [RGBD Object Tracking: An In-depth Review](https://mlsanity.com/paper/2203.14134/) (2022)
- [Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking](https://mlsanity.com/paper/2108.04521/) (2021)
- [Visual Object Tracking on Multi-modal RGB-D Videos: A Review](https://mlsanity.com/paper/2201.09207/) (2022)
- [Omni Survey for Multimodality Analysis in Visual Object Tracking](https://mlsanity.com/paper/2508.13000/) (2025)
- [Transparent Object Tracking with Enhanced Fusion Module](https://mlsanity.com/paper/2309.06701/) (2023)
- [Depth Attention for Robust RGB Tracking](https://mlsanity.com/paper/2410.20395/) (2024)

## Abstract (arXiv)

Depth information provides a strong cue for occlusion detection and handling, but has been largely omitted in generic object tracking until recently due to lack of suitable benchmark datasets and applications. In this work, we propose a Depth Masked Discriminative Correlation Filter (DM-DCF) which adopts novel depth segmentation based occlusion detection that stops correlation filter updating and depth masking which adaptively adjusts the spatial support for correlation filter. In Princeton RGBD Tracking Benchmark, our DM-DCF is among the state-of-the-art in overall ranking and the winner on multiple categories. Moreover, since it is based on DCF, ``DM-DCF`` runs an order of magnitude faster than its competitors making it suitable for time constrained applications.