Acceso abierto

Long-term Target Tracking Based on Template Updating and Redetection

Shuping Xu

Shuping Xu

School of Computer Science and Engineering, Xi’an Technological UniversityXi’an, China
Buscar este autor en
Sciendo|Google Scholar
Xu, Shuping
 y 
Yinglong Li

Yinglong Li

School of Computer Science and Engineering, Xi’an Technological UniversityXi’an, China
Buscar este autor en
Sciendo|Google Scholar
Li, Yinglong
  
31 dic 2024
Long-term Target Tracking Based on Template Updating and Redetection's Cover Image
Acerca de este artículo
Artículo anterior
Artículo siguiente
Cite
Descargar portada
Publicado en línea: 31 dic 2024
Páginas: 35 - 47
DOI: https://doi.org/10.2478/ijanmc-2024-0035
Palabras clave
© 2024 Shuping Xu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Block diagram of long-term target tracking algorithm
Block diagram of long-term target tracking algorithm

Figure 2.

Template updater based on state judgment
Template updater based on state judgment

Figure 3.

Confidence score chart
Confidence score chart

Figure 4.

SiamRPN tracking results
SiamRPN tracking results

Figure 5.

Flow chart of target loss judgment mechanism
Flow chart of target loss judgment mechanism

Figure 6.

Template-based re-detection algorithm
Template-based re-detection algorithm

Figure 7.

Different ts corresponding success rates and accuracy
Different ts corresponding success rates and accuracy

Figure 8.

Results of parameter optimization
Results of parameter optimization

Figure 9.

Diagram of LaSOT experimental results
Diagram of LaSOT experimental results

Figure 10.

Results of qualitative analysis of bird1 video sequence
Results of qualitative analysis of bird1 video sequence

Figure 11.

yamaha video sequence qualitative analysis results
yamaha video sequence qualitative analysis results

Figure 12.

Ablation experiment results
Ablation experiment results

Experimental Results on VOT2018_LT

Algorithm F-value Accuracy Frame rate Recall rate
SiamFC 0.429 0.628 84 0.323
MBMD 0.613 0.636 4 0.576
DASiamRPN_LT 0.604 0.625 63 0.585
SPLT 0.614 0.629 26 0.602
SiamRPN++ 0.625 0.646 35 0.606
Ours 0.644 0.659 28 0.626

Introduction of 2 groups of video sequences

Video Sources Name Number of frames Type of challenge
VOT2018_LT Yamaha 3143 Out of sight, occlusion, deformation
VOT2018_LT bird1 2437 Analogue interference, out of view, blocking
Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Informática, Informática, otros
RSS Feed de revista