Moving dim-target tracking algorithm using template matching

Lee, Eun-Young; Yoo, Hyun-Jung; Gu, Eun-Hye; Park, KilHoum

doi:10.1109/taeece.2013.6557289

Cited by 1 publication

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Huang et al [2] proposed an efficient small target location algorithm initialized using a strong detector created from the shape analysis of foreground spots and a particle filter-based tracker that can handle the fuzziness of template matching. The improved template matching algorithm (TMT), proposed by Ruiming Liu et al [3,4], performs well by calculating correlation coefficients in high-dimensional feature spaces. However, when the background clutter is strong, it is difficult to track the small target accurately for a long time.…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Tracking Based on OSTrack Model

Shan,

Yang,

Liu

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Infrared small target tracking plays an important role in military reconnaissance, early warning, video surveillance, and civil applications. For tracking small infrared targets in this paper, a one-stream deep learning model is utilized. In order to integrate the processes of feature extraction and feature fusion, the model uses transformer as the framework's major component and creates a bidirectional information flow between the template and the search picture pairs in the feature extraction stage. Use the head of the model to get the target position. Finally, post-processing of the target area, including tracking success, saves the coordinate information of the target frame; tracking failure, near, in the middle, and far from the target box, searches for the real target. It helps to solve the situation where the target moves fast and encounters a complex background to achieve better tracking results. It is tested on an infrared small target data set, and the results show that the method in this paper reaches 80.50% average tracking accuracy. The image sequences in the data set include sky, sea, and buildings. Tracking video and original images are shown at https://github.com/AHUT507LAB/Infrared-dim-small-target-tracking.INDEX TERMS Infrared small target, Small target tracking, Post-processing, Deep learning

show abstract

Section: Introductionmentioning

confidence: 99%