A Review of Object Detection and Tracking Methods

doi:10.21090/ijaerd.45913

Cited by 9 publications

(2 citation statements)

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“…State vectors describe the dynamic behavior of a system, such as the position and velocity of objects. The general framework for dynamic pattern estimation problems is taken from Bayesian methods [3] . For learning based methods, the features and appearances of different targets, as well as their predictions, are learned in the next frame.…”

Section: Introductionmentioning

confidence: 99%

Visual object tracking for mobile robots based on SAM-TRACK

Liu,

Zhang

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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The application of visual object tracking SAM-TRACK is widespread. For the problem of robot positioning and navigation, a single laser sensor obtains less information. Adding a depth camera to provide visual information will enhance the robustness of the system. Design a system for visual object tracking based on laser SLAM. Identify and segment targets by clicking, drawing edges, and inputting text, and calculate the target position by comparing the segmentation results with the depth map. At the same time, track the target in real-time to control the robot to ultimately reach the target position. Experiments have shown that multiple input methods can effectively achieve segmentation; By comparing with SiamRPN, it has been proven that the system is more efficient.

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Section: Introductionmentioning

confidence: 99%

Visual object tracking for mobile robots based on SAM-TRACK

Liu,

Zhang

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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“…Additionally, the human brain has the capacity to extrapolate its findings from a collection of things and reliably recognize ones it hasn't yet seen [3]. Scientists working in the fields of machine vision and computer science have used the study of the human brain's cognitive capacity to recognize and draw inspiration from it to design and present a variety of object recognition systems [4]. In order to organize visual information, these findings are also used in related applications like picture concept recovery and image indexing [5].…”

Section: Introductionmentioning

confidence: 99%

A Cost-Effective Method for Detecting and Tracking Moving Objects using Overlapping Methods

ZHANG

2023

IJACSA

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Overlay approaches for moving object detection and tracking have recently received attention as a crucial field for computer science and computer vision research. Using pixel overlap and visual attributes, these techniques enable the recognition and tracking of objects in movies or video data. Two color and edge features for the suggested method are presented in this article. The suggested approach uses the SED algorithm, and since the edges have a lower volume than the entire image, the processing process will be faster with the reduction of information. The characteristic of color is the HSV (hue, saturation and value) histogram because it is close to human vision. However, because the margins tidy up the shapes in the human eye, they contain important information. These concerns lead to the conclusion that the histogram of gradient angles based on regional binary patterns is the edge feature of the suggested system. There are two justifications for employing local binary patterns. First, the principal edges are emphasized by using local binary patterns. Another point is that the image produced by this method displays the image's texture; in other words, the shape's feature is taken from the context of the texture, which is regarded as a type of combination of features. Several criteria were evaluated in order to assess the suggested approach for tracking images in comparison to related systems; the most significant of these are the precision, recall, and similarity criteria. In comparison to other works, the findings for precision have generally increased accuracy by 25%, recall by 17%, and similarity by 12%.

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