Object Tracking with Channel Group Regularization and Smooth Constraints Using Improved Dynamic Convolution Kernels in ITS

Aiming at the problem that the correlation between multi-channel feature representation and filter structure is not considered in the objective function modeling, a object tracking algorithm with channel group regularization and time series smooth constraint using improved dynamic convolution kernel...

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Veröffentlicht in:	Multimedia tools and applications Jg. 84; H. 29; S. 35191 - 35215
Hauptverfasser:	Sun, Jinping, Li, Dan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.09.2025 Springer Nature B.V
Schlagworte:	1231: IoT-driven Computer Vision Technology for Smart Transportation Applications Accuracy Algorithms Artificial neural networks Channels Computer Communication Networks Computer Science Constraints Correlation Data Structures and Information Theory Feature maps Intelligent transportation systems Localization Multimedia Information Systems Neural networks Object recognition Process controls Regularization Spatial data Special Purpose and Application-Based Systems Task complexity Tracking Vision systems Time series smoothing Sparse representation Correlation filter Object tracking Dynamic convolution kernel
ISSN:	1573-7721, 1380-7501, 1573-7721
Online-Zugang:	Volltext
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