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Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters

This is the Matlab side of the implementation for the paper published in the proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.

Publication

Uğur Kart, Alan Lukežič, Matej Kristan, Joni-Kristian Kämäräinen and Jiří Matas. ''Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters.'' In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.</br>

<b>BibTex citation:</b><br> @InProceedings{Kart_CVPR_2019,<br> Title = {Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters},<br> Author = {Kart, Uğur and Lukežič, Alan and Kristan, Matej and Kämäräinen, Joni-Kristian and Matas, Jiří},<br> Booktitle = {CVPR},<br> Year = {2019}<br> }

Contact

Uğur Kart, e-mail: ugur.kart@tuni.fi </br>

Installation and demo

• Our method works by running the Matlab and C++ code (a modified version of Co-Fusion) simultaneously. Therefore, both of them need to be present on the same machine (can be also done on separate machines but we haven't tried that) and the necessary paths are set correctly.
• Clone git repositories: </br> $ git clone https://github.com/ugurkart/otr.git $ git clone https://github.com/ugurkart/co-fusion.git
• Install Co-Fusion and its dependencies
• Compile mex files running compile.m command </br> Set <i>opencv_include</i> and <i>opencv_libpath</i> to the correct OpenCV paths
• Please note that original PTB has many synchronization and registration errors. Therefore we use the depth images provided by Bibi et al. CVPR 2016.
• Build Co-Fusion into a binary
• Set necessary paths both on Matlab and Co-Fusion side. Make sure to set correct calibration parameters for Co-Fusion.
• Use run_csr.m script for the visualization of the tracker and to replicate our results reported in the paper. </br>

Project summary

A major limitation of the standard RGB-D trackers is that the target is inherently considered as a 2D structure, which makes dealing with appearance changes related even to a simple out-of-plane rotation highly challenging. We propose a novel long-term RGB-D tracker – Object Tracking by Reconstruction (OTR) – which is based on online 3D reconstruction of the tracked target to learn a set of view-specific discriminative correlation filters (DCFs). The 3D reconstruction provides two important cues: (i) its 2D projection generates an accurate spatial support for constrained DCF learning and (ii) point-cloud based estimation of 3D pose change is used to select and store DCFs associated with specific object viewpoints. OTR is extensively evaluated on two challenging RGB-D benchmarks, Princeton Tracking Benchmark and the STC Benchmark, and sets the new state-of-the-art by a large margin to the current top performers on these benchmarks.