TY - JOUR
T1 - Performance evaluation metrics and statistics for positional tracker evaluation
AU - Needham, Chris J.
AU - Boyle, Roger D.
PY - 2003
Y1 - 2003
N2 - This paper discusses methods behind tracker evaluation, the aim being to evaluate how well a tracker is able to determine the position of a target object. Few metrics exist for positional tracker evaluation; here the fundamental issues of trajectory comparison are addressed, and metrics are presented which allow the key features to be described. Often little evaluation on how precisely a target is tracked is presented in the literature, with results detailing for what percentage of the time the target was tracked. This issue is now emerging as a key aspect of tracker performance evaluation. The metrics developed are applied to real trajectories for positional tracker evaluation. Data obtained from a sports player tracker on video of a 5-a-side soccer game, and from a vehicle tracker, is analysed. These give quantitative positional evaluation of the performance of computer vision tracking systems, and provides a framework for comparison of different methods and systems on benchmark data sets.
AB - This paper discusses methods behind tracker evaluation, the aim being to evaluate how well a tracker is able to determine the position of a target object. Few metrics exist for positional tracker evaluation; here the fundamental issues of trajectory comparison are addressed, and metrics are presented which allow the key features to be described. Often little evaluation on how precisely a target is tracked is presented in the literature, with results detailing for what percentage of the time the target was tracked. This issue is now emerging as a key aspect of tracker performance evaluation. The metrics developed are applied to real trajectories for positional tracker evaluation. Data obtained from a sports player tracker on video of a 5-a-side soccer game, and from a vehicle tracker, is analysed. These give quantitative positional evaluation of the performance of computer vision tracking systems, and provides a framework for comparison of different methods and systems on benchmark data sets.
UR - http://www.scopus.com/inward/record.url?scp=84886635386&partnerID=8YFLogxK
U2 - 10.1007/3-540-36592-3_27
DO - 10.1007/3-540-36592-3_27
M3 - Article
AN - SCOPUS:84886635386
SN - 0302-9743
VL - 2626
SP - 278
EP - 289
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -