Introducing Depth Information Into Generative Target Tracking

Abstract

Common visual features used in target tracking, including colour and grayscale, are prone to failure in a confusingly similar-looking background. As the technology of three-dimensional visual information acquisition has gradually gained ground in recent years, the conditions for the wide use of depth information in target tracking has been made available. This study focuses on discussing the possible ways to introduce depth information into the generative target tracking methods based on a kernel density estimation as well as the performance of different methods of introduction, thereby providing a reference for the use of depth information in actual target tracking systems. First, an analysis of the mean-shift technical framework, a typical algorithm used for generative target tracking, is described, and four methods of introducing the depth information are proposed, i.e., the thresholding of the data source, thresholding of the density distribution of the dataset applied, weighting of the data source, and weighting of the density distribution of the dataset. Details of an experimental study conducted to evaluate the validity, characteristics, and advantages of each method are then described. The experimental results showed that the four methods can improve the validity of the basic method to a certain extent and meet the requirements of real-time target tracking in a confusingly similar background. The method of weighting the density distribution of the dataset, into which depth information is introduced, is the prime choice in engineering practise because it delivers an excellent comprehensive performance and the highest level of accuracy, whereas methods such as the thresholding of both the data sources and the density distribution of the dataset are less time-consuming. The performance in comparison with that of a state-of-the-art tracker further verifies the practicality of the proposed approach. Finally, the research results also provide a reference for improvements in other target tracking methods in which depth information can be introduced.

A Siamese tracker with “dynamic–static” dual-template fusion and dynamic template adaptive update

“…Video object tracking, which refers to continuously tracking the state of an object in subsequent frame sequences by using the initial position and scale information of the object, is the basis for high-level visual tasks such as visual inspection, visual navigation, and visual servo (Nousi et al, 2020 ; Wang et al, 2020 ; Karakostas et al, 2021 ; Sun et al, 2021 ). In engineering practice, interference such as changes in the posture and scale of the object, noise interference, background occlusion, or variation of light conditions may lead to tracking failure, so object tracking remains a challenging task (Zhang et al, 2020 ; Zhang H. et al, 2021 ; Liu et al, 2022 ).…”

3-D Refuse-Derived Fuel Particle Tracking-by-Detection Using a Plenoptic Camera System

“…Under this circumstance, direct 3D tracking is impracticable. However, the acquired depth information can be utilized implicitly, as presented in [33], where the depth information is considered a threshold factor. This new concept deals with relatively unsteady and inaccurate 3D information in object tracking, for instance, 3D information gathered in complex industrial processes.…”

Siamese Tracker with “Dynamic-static” Dual-template Fusion and Dynamic Template Adaptive Update

“…Video object tracking, which refers to continuously tracking the state of the object in subsequent frame sequences by using the initial position and scale information of the object, is the basis for high-level visual tasks such as visual inspection, visual navigation and visual servo [1][2][3][4]. In engineering practice, interference such as changes in the posture and scale of the object, noise interference, background occlusion or variation of light condition may lead to tracking failure, so object tracking is still a challenging task [5][6][7].…”