.png){kind=icon}
Return
Hybridizing Teaching-Learning-Based Optimization With Adaptive Grasshopper Optimization Algorithm for Abrupt Motion Tracking
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2954500.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Aiming at the problem that conventional tracking algorithms are difficult to deal with abrupt motion efficiently, an optimization algorithm called hybrid Teaching-learning-based optimization with Adaptive Grasshopper Optimization Algorithm (TLGOA) is proposed in this paper. Firstly, the non-linear strategy based on tangent function is used to replace the linear mechanism in the standard Grasshopper Optimization Algorithm (GOA). The improved adaptive GOA (AGOA) can avoid the local trapping problem and enhance the global optimization ability, which can handle the problem of abrupt motion. Secondly, considering that Teaching-learning-based optimization (TLBO) has obviously local exploitation operator and fast convergence, a hybrid TLGOA tracker is designed by combining the advantages of both AGOA and TLBO. The approach can enable better tracking accuracy and efficiency. Finally, extensive experimental results show that the proposed algorithm has obvious advantages over other algorithms, and also prove that TLGOA tracker is very competitive compared to other state-of-the-art trackers, especially for abrupt motion tracking.
Keywords:
Visual tracking
abrupt motion
grasshopper optimization algorithm
teaching-learning-based optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
ShareSaveShareSaveAn Experimental Comparison of Swarm Optimization Based Abrupt Motion Tracking Methods.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSaveShareSaveA hybrid mobile object tracker based on the modified Cuckoo Search algorithm and the Kalman FilterPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSave
View more