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Short Description
Robust Structured Tracking code
Full Description

Antrack

Antrack is an open source implementation of the Structured Tracker and tracking evaluation suite. The original structured tracker was introduced by Hare et. al. 2011 and achieved state-of-the-art in the benchmark by Wu et. al 2012. This implementation extends the structured tracker by adding Robust Kalman filter and objectness priors. Each extension is independent of each other and improves tracking metrics on Wu et. al. 2012 dataset.

Installation

Dependencies installation was tested on Ubuntu 14.04. Mac OS X 10.11 support is experimental.

Ubuntu 14.04

Script install_dependencies.sh will install all dependencies automatically. To compile the code it is neccessary to have compiler which
accepts c++11 flag (gcc > 4.9). To install necessary compiler see gist.

To compile do:

./install_dependencies.sh

Mac OS X 10.11

Script install_dependencies.sh will try to install dependencies using homebrew.

List of dependencies

Using

C++ interface

Coming soon

Python interface

Coming soon.

Reproducing VOT 2015

VOT 2015 is a popular dataset for tracking evaluation with it's own evaluatin protocol. To evaluate our tracker on VOT 2015 download evaluation toolkit. Compile the tracker using:

mkdir build
cd build
cmake -DVOT2015=ON ..
# should create $Antrack/matlab/build/bin/struck_vot2014 binary
# example of the file to use with VOT2015 is in #Antrack/matlab/tracker_RobStruck.m

For further details see how to integrate the tracker with the tookit click here.

Reproducing results on Wu et. al. 2013 dataset

Compiling

mkdir build
cd build
cmake -DCVPR2016=ON ..
make -j8

Running

See more examples in the scripts/ folder.

./cvpr2016 \        
    --datasetSaveLocation=${datasetSaveLocation}\ --filter=${filter} \ # use robust filter or not RobStruck/Struck
    --nThreads=${nThreads} \ # number of threads to use
    --updateEveryNframes=${updateEveryNFrames} \ # how often the tracker should update
    --b=${b} --P=${P_param} --Q=${Q} --R=${R} \ # Robust Kalman Filter parameters
    --feature=${feature} --kernel=${kernel} \ # Feature-kernel pairs; for best results use hogANDhist & int
    --prefix=${prefix} \ # prefix when saving results of the files
    --lambda_s=${lambda_s} \        # straddling lambda use 0
    --lambda_e=${lambda_e} \ # edge density lambda use 0.4
    --inner=${inner} \ # inner scale parameter; best - 0.9
    --straddeling_threshold=${straddeling_threshold} \ # threshold defining when to cutoff straddling; best: 1.5
    --experiment_type=${experiment_type} \ # 0 if evaluation should be performed on 50 videos, 1 if SRE+TRE should be performed (1632 video runs)
    --tracker_type=${tracker_type}\ # 0 - RobStruck, 1 - ObjStruck
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gnattuha
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