See original README. Provides functions to run tracker evaluation in parallel.
Usage
- Default (for all trackers, all sequences, all evaltypes(OPE, SRE, TRE))
- command : python run_trackers.py
- same with
- For specific trackers, sequences, evaltypes
- command : python run_trackers.py -t "tracker" -s "sequence" -e "evaltype"
- e.g : python run_trackers.py -t IVT,TLD -s Couple,Crossing -e OPE,SRE)
Libraries
-
Matlab Engine for python (only needed for executing matlab script files of trackers)
https://kr.mathworks.com/help/matlab/matlab-engine-for-python.html
-
matplotlib
-
numpy
-
Python Imaging Library (PIL)
Original benchmark evaluation has 2 drawbacks. First, it only evaluates the trackers sequentially. Second, results are saved only in the end. run_trackers_cached_parallel.py
allows to save evaluation for each video independently; it also allows to run the evaluation in parallel. Syntax is as follows:
python run_trackers_cached_parallel.py -t RobStruck -e OPE -p 16 # runs evaluation of the RobStruck tracker using OPE protocol with 16 threads
# it is possible to run more than one tracker
python run_trackers_cached_parallel.py -t RobStruck,ObStruck,MBestStruck -e OPE -p 16
-
Sequences have to be downloaded either manually or using original benchmark (original). Once its done a symlink ./data/ -> place where sequences are downloaded would suffice.
-
Evaluations on SRE,TRE have different parameters than the MATLAB toolbox. This results that during evaluation results needed to be trimmed.