# LADCF

**Repository Path**: greitzmann/LADCF

## Basic Information

- **Project Name**: LADCF
- **Description**: Matlab implementation of TIP2019 paper "Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking"
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-11-14
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# LADCF - No 1 Algorithm on the public dataset of [VOT2018](http://www.votchallenge.net/vot2018/)
Demo for Learning Adaptive Discriminative Correlation Filters (LADCF) via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking

## [Download the Paper](https://ieeexplore.ieee.org/document/8728173)
>@article{xu2019learning,
 title={Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking},
 author={Xu, Tianyang and Feng, Zhen-Hua and Wu, Xiao-Jun and Kittler, Josef},
 journal={IEEE Transactions on Image Processing},
 pages={5596--5609},
 volume={28},
 number={11},
 year={2019}
}

## The tracker codes for ICCV2019 can be [download here](https://github.com/XU-TIANYANG/GFS-DCF). 
More group feature selection strategies are explored.

### The tracker codes for VOT2018 can be [download here](https://github.com/XU-TIANYANG/LADCF_VOT). 
More powerful features and data augmentation techniques are added for the VOT2018.

### Instruction for LADCF_HC Tracker:
Learning Adaptive Discriminative Correlation Filter on Low-dimensional Manifold (LADCF) utilises adaptive spatial regularizer to train low-dimensional discriminative correlation filters. We follow a single-frame learning and updating strategy: the filters are learned after tracking stage and then updated using a fixed rate [1]. We use HOG [2] and CN [3]. Code modules refer to ECO [4] in feature extraction.

#### Dependencies:
- [PDollar Toolbox](https://pdollar.github.io/toolbox)
- mtimesx (https://github.com/martin-danelljan/ECO/tree/master/external_libs/mtimesx)
- mexResize (https://github.com/martin-danelljan/ECO/tree/master/external_libs/mexResize) 

#### Operating system:
Ubuntu 14.04 LTS, Matlab R2016a, CPU Intel(R) Xeon(R) E5-2643 

#### References:
- [1] Henriques, João F., et al. "High-speed tracking with kernelized correlation filters." IEEE Transactions on Pattern Analysis and Machine Intelligence 37.3 (2015): 583-596.
- [2] Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." Computer Vision and Pattern Recognition, 2005. CVPR 2005. 
- [3] Van De Weijer, Joost, et al. "Learning color names for real-world applications." IEEE Transactions on Image Processing 18.7 (2009): 1512-1523.
- [4] Danelljan, Martin, et al. "Eco: Efficient convolution operators for tracking." Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

#### Raw Results:
[OTB100(hand-crafted feature)](https://github.com/XU-TIANYANG/cakes/raw/master/LADCF_HC_OTB100_results.zip)
[OTB100(deep feature)](https://github.com/XU-TIANYANG/cakes/raw/master/LADCF_OTB100_results.zip)