XU-TIANYANG / LADCF

Matlab implementation of TIP2019 paper "Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking"

LADCF - No 1 Algorithm on the public dataset of VOT2018

Demo for Learning Adaptive Discriminative Correlation Filters (LADCF) via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking

Download the Paper

@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.

More group feature selection strategies are explored.

The tracker codes for VOT2018 can be download here.

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
• 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) OTB100(deep feature)