TY - GEN
T1 - Symbiotic Trackers’ Ensemble with Trackers’ Re-initialization for Face Tracking
AU - Ayma, Victor H.
AU - Happ, Patrick N.
AU - Feitosa, Raul Q.
AU - Costa, Gilson A.O.P.
AU - Feijó, Bruno
N1 - Funding Information:
This work was supported by CAPES of the Ministry of Education and CNPq of the Ministry of Science, Technology, Innovation and Communication, Brazil.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Visual object tracking aims to deliver accurate estimates about the state of the target in a sequence of images or video frames. Nevertheless, tracking algorithms are sensitive to different kinds of image perturbations that frequently cause tracking failures. Indeed, tracking failures result from the insertion of imprecise target-related data into the trackers’ appearance models, which leads the trackers to lose the target or drift away from it. Here, we propose a tracking fusion approach, which incorporates feedback and re-initialization mechanisms to improve overall tracking performance. Our fusion technique, called SymTE-TR, enhances trackers’ overall performance by updating their appearances models with reliable information of the target’s states, while resets the imprecise trackers. We evaluated our approach on a facial video dataset, which characterizes a particular challenging tracking application under different imaging conditions. The experimental results indicate that our approach contributes to enhancing individual tracker performances by providing stable results across the video sequences and, consequently, contributes to stable overall tracking fusion performances.
AB - Visual object tracking aims to deliver accurate estimates about the state of the target in a sequence of images or video frames. Nevertheless, tracking algorithms are sensitive to different kinds of image perturbations that frequently cause tracking failures. Indeed, tracking failures result from the insertion of imprecise target-related data into the trackers’ appearance models, which leads the trackers to lose the target or drift away from it. Here, we propose a tracking fusion approach, which incorporates feedback and re-initialization mechanisms to improve overall tracking performance. Our fusion technique, called SymTE-TR, enhances trackers’ overall performance by updating their appearances models with reliable information of the target’s states, while resets the imprecise trackers. We evaluated our approach on a facial video dataset, which characterizes a particular challenging tracking application under different imaging conditions. The experimental results indicate that our approach contributes to enhancing individual tracker performances by providing stable results across the video sequences and, consequently, contributes to stable overall tracking fusion performances.
KW - Face tracking
KW - Online object tracking
KW - Tracking fusion
KW - Tracking re-initialization
UR - http://www.scopus.com/inward/record.url?scp=85111103684&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-76228-5_18
DO - 10.1007/978-3-030-76228-5_18
M3 - Articulo (Contribución a conferencia)
AN - SCOPUS:85111103684
SN - 9783030762278
T3 - Communications in Computer and Information Science
SP - 250
EP - 263
BT - Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
A2 - Lossio-Ventura, Juan Antonio
A2 - Valverde-Rebaza, Jorge Carlos
A2 - Díaz, Eduardo
A2 - Alatrista-Salas, Hugo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Annual International Conference on Information Management and Big Data, SIMBig 2020
Y2 - 1 October 2020 through 3 October 2020
ER -