TY - GEN
T1 - Mixing hough and color histogram models for accurate real-time object tracking
AU - Tran, Antoine
AU - Manzanera, Antoine
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - This paper presents a new object tracking algorithm, which does not rely on offline supervised learning. We propose a very fast and accurate tracker, exclusively based on two complementary low-level features: gradient-based and color-based features. On the first hand, we compute a Generalized Hough Transform, indexed by gradient orientation. On the second hand, a RGB color histogram is used as a global rotation-invariant model. These two parts are processed independently, then merged to estimate the object position. Then, two confidence maps are generated and combined to estimate the object size. Experiments made on VOT2014 and VOT2015 datasets show that our tracker is competitive among all competitors (in accuracy and robustness, ranked in the top 10 and top 15 respectively), and is one of the few trackers running at more than 100, fps on a laptop machine, with one thread. Thanks to its low memory footprint, it can also run on embedded systems.
AB - This paper presents a new object tracking algorithm, which does not rely on offline supervised learning. We propose a very fast and accurate tracker, exclusively based on two complementary low-level features: gradient-based and color-based features. On the first hand, we compute a Generalized Hough Transform, indexed by gradient orientation. On the second hand, a RGB color histogram is used as a global rotation-invariant model. These two parts are processed independently, then merged to estimate the object position. Then, two confidence maps are generated and combined to estimate the object size. Experiments made on VOT2014 and VOT2015 datasets show that our tracker is competitive among all competitors (in accuracy and robustness, ranked in the top 10 and top 15 respectively), and is one of the few trackers running at more than 100, fps on a laptop machine, with one thread. Thanks to its low memory footprint, it can also run on embedded systems.
UR - https://www.scopus.com/pages/publications/85028503379
U2 - 10.1007/978-3-319-64689-3_4
DO - 10.1007/978-3-319-64689-3_4
M3 - Conference contribution
AN - SCOPUS:85028503379
SN - 9783319646886
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 43
EP - 54
BT - Computer Analysis of Images and Patterns - 17th International Conference, CAIP 2017, Proceedings
A2 - Heyden, Anders
A2 - Felsberg, Michael
A2 - Kruger, Norbert
PB - Springer Verlag
T2 - 17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017
Y2 - 22 August 2017 through 24 August 2017
ER -