TY - GEN
T1 - Adaptive window strategy for high-speed and robust KLT feature tracker
AU - Ramakrishnan, Nirmala
AU - Srikanthan, Thambipillai
AU - Lam, Siew Kei
AU - Tulsulkar, Gauri Ravindra
PY - 2016/1/1
Y1 - 2016/1/1
N2 - The Kanade-Lucas-Tomasi tracking (KLT) algorithm is widely used for local tracking of features. As it employs a translation model to find the feature tracks, KLT is not robust in the presence of distortions around the feature resulting in high inaccuracies in the tracks. In this paper we show that the window size in KLT must vary to adapt to the presence of distortions around each feature point in order to increase the number of useful tracks and minimize noisy ones. We propose an adaptive window size strategy for KLT that uses the KLT iterations as an indicator of the quality of the tracks to determine near-optimal window sizes, thereby significantly improving its robustness to distortions. Our evaluations with a well-known tracking dataset show that the proposed adaptive strategy outperforms the conventional fixed-window KLT in terms of robustness. In addition, compared to the well-known affine KLT, our method achieves comparable robustness at an average runtime speedup of 7x.
AB - The Kanade-Lucas-Tomasi tracking (KLT) algorithm is widely used for local tracking of features. As it employs a translation model to find the feature tracks, KLT is not robust in the presence of distortions around the feature resulting in high inaccuracies in the tracks. In this paper we show that the window size in KLT must vary to adapt to the presence of distortions around each feature point in order to increase the number of useful tracks and minimize noisy ones. We propose an adaptive window size strategy for KLT that uses the KLT iterations as an indicator of the quality of the tracks to determine near-optimal window sizes, thereby significantly improving its robustness to distortions. Our evaluations with a well-known tracking dataset show that the proposed adaptive strategy outperforms the conventional fixed-window KLT in terms of robustness. In addition, compared to the well-known affine KLT, our method achieves comparable robustness at an average runtime speedup of 7x.
UR - http://www.scopus.com/inward/record.url?scp=84958971831&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958971831&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-29451-3_29
DO - 10.1007/978-3-319-29451-3_29
M3 - Conference contribution
AN - SCOPUS:84958971831
SN - 9783319294506
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 355
EP - 367
BT - Image and Video Technology - 7th Pacific-Rim Symposium, PSIVT 2015, Revised Selected Papers
PB - Springer Verlag
T2 - 7th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2015
Y2 - 25 November 2015 through 27 November 2015
ER -