Yannan Cai, Ke Tan, Zhenzhong Wei^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1621-1640, 2023, DOI:10.32604/cmc.2023.036177 - 06 February 2023

Abstract Label assignment refers to determining positive/negative labels for each sample to supervise the training process. Existing Siamese-based trackers primarily use fixed label assignment strategies according to human prior knowledge; thus, they can be sensitive to predefined hyperparameters and fail to fit the spatial and scale variations of samples. In this study, we first develop a novel dynamic label assignment (DLA) module to handle the diverse data distributions and adaptively distinguish the foreground from the background based on the statistical characteristics of the target in visual object tracking. The core of DLA module is a two-step… More >

Shaozhe Guo¹, Yong Li^1,*, Xuyang Chen², Youshan Zhang¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3137-3148, 2022, DOI:10.32604/cmc.2022.026784 - 16 June 2022

Abstract The Visual tracking problem can usually be solved in two parts. The first part is to extract the feature of the target and get the candidate region. The second part is to realize the classification of the target and the regression of the bounding box. In recent years, Siameses network in visual tracking problem has always been a frontier research hotspot. In this work, it applies two branches namely search area and tracking template area for similar learning to track. Some related researches prove the feasibility of this network structure. According to the characteristics of… More >

Yu Wang^*

Journal on Artificial Intelligence, Vol.3, No.2, pp. 63-72, 2021, DOI:10.32604/jai.2021.010455 - 08 May 2021

Abstract Visual tracking is a classical computer vision problem with many applications. Efficient convolution operators (ECO) is one of the most outstanding visual tracking algorithms in recent years, it has shown great performance using discriminative correlation filter (DCF) together with HOG, color maps and VGGNet features. Inspired by new deep learning models, this paper propose a hybrid efficient convolution operators integrating fully convolution network (FCN) and residual network (ResNet) for visual tracking, where FCN and ResNet are introduced in our proposed method to segment the objects from backgrounds and extract hierarchical feature maps of objects, respectively. More >

Detian Huang¹, Peiting Gu², Hsuan-Ming Feng^3,*, Yanming Lin¹, Lixin Zheng¹

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 313-322, 2020, DOI:10.31209/2019.100000105

Abstract To tackle the problem of illumination sensitive, scale variation, and occlusion in the Kernelized Correlation Filters (KCF) tracker, an improved robust tracking algorithm based on KCF is proposed. Firstly, the color attribute was introduced to represent the target, and the dimension of target features was reduced adaptively to obtain low-dimensional and illumination-insensitive target features with the locally linear embedding approach. Secondly, an effective appearance model updating strategy is designed, and then the appearance model can be adaptively updated according to the Peak-to-Sidelobe Ratio value. Finally, the low-dimensional color features and the HOG features are utilized More >

Zhenguo Gao¹, Shixiong Xia¹, Yikun Zhang¹, Rui Yao^1,*, Jiaqi Zhao¹, Qiang Niu¹, Haifeng Jiang²

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 509-521, 2018, DOI:10.3970/cmc.2018.02634

Abstract The colour feature is often used in the object tracking. The tracking methods extract the colour features of the object and the background, and distinguish them by a classifier. However, these existing methods simply use the colour information of the target pixels and do not consider the shape feature of the target, so that the description capability of the feature is weak. Moreover, incorporating shape information often leads to large feature dimension, which is not conducive to real-time object tracking. Recently, the emergence of visual tracking methods based on deep learning has also greatly increased… More >