A Lightweight Face Tracking System for Video Surveillance

Oleinik, Andrei

doi:10.1007/978-3-319-41501-7_46

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…With the advancement of technology, image matching techniques have become increasingly important in a variety of applications, including military affairs [7], medicine [8], industry [9], license plate recognition [10], fingerprint recognition [11,12], face recognition [13], animal motion trajectory tracking system [14], and face tracking shooting system [15]. Image matching represents a principal aspect of many problems in computer vision, including motion tracking [16], object recognition and matching [17,18], 3D reconstruction [19], stereo correspondence [20], image classification and retrieval [21], and camera calibration [22].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Stereo Images Matching

Hadi

Khalaf

2021

E3S Web Conf.

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Image matching and finding correspondence between a stereo image pair is an essential task in digital photogrammetry and computer vision. Stereo images represent the same scene from two different perspectives, and therefore they typically contain a high degree of redundancy. This paper includes an evaluation of implementing manual as well as auto-match between a pair of images that acquired with an overlapped area. Particular target points are selected to be matched manually (22 target points). Auto-matching, based on feature-based matching (FBM) method, has been applied to these target points by using BRISK, FAST, Harris, and MinEigen algorithms. Auto matching is conducted with two main phases: extraction (detection and description) and matching features. The matching techniques used by the prevalent algorithms depend on local point (corner) features. Also, the performance of the algorithms is assessed according to the results obtained from various criteria, such as the number of auto-matched points and the target points that auto-matched. This study aims to determine and evaluate the total root mean square error (RMSE) by comparing coordinates of manual matched target points with those obtained from auto-matching by each of the algorithms. According to the experimental results, the BRISK algorithm gives the higher number of auto-matched points, which equals 2942, while the Harris algorithm gives 378 points representing the lowest number of auto-matched points. All target points are auto-matched with BRISK and FAST algorithms, while 3 and 9 target points only auto-matched with Harris and MinEigen algorithms, respectively. Total RMSE in its minimum value is given by FAST and manual match in the first image, it is 0.002651206 mm, and Harris and manual match provide the minimum value of total RMSE in the second image is 0.002399477 mm.

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