Object Tracking with Channel Group Regularization and Smooth Constraints Using Improved Dynamic Convolution Kernels in ITS - Sun, Jinping; Li, Dan (Multimedia tools and applications, 2025)
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 kernels is proposed. Firstly, the elements in the filter are grouped using spatial and channel properties, the time-domain correlation of the object model is constrained by the low-rank kernel norm. A group regularization term is constructed to describe the correlation between channels with a mixed-norm structured sparsity constraint to learn lower-dimensional filters. Then, after extracting some channels in the feature map, part of the spatial information of each channel is retained to generate an efficient dynamic convolution kernel through the channel information de-redundant attention mechanism to obtain an optimized lightweight convolutional neural network. Finally, combining the advantages of hand-crafted features and deep convolutional features, the complementary localization of the object from coarse to fine is realized with the help of the constructed efficient feature model and the learned filter. The experimental results on public datasets show that the proposed algorithm can adapt to the tracking tasks of various complex traffic scenes, and enhance the tracking performance of the existing models. The proposed algorithm improves the discriminative property of the object model and the self-adaptability of the spatio-temporal information of the dynamic convolution kernel, and can be applied to neuromorphic vision system of intelligent transportation systems.