A Comparative Study of different Oject Tracking Methods in a Video

Almohaimeed, Norah; Prince, Master

doi:10.5120/ijca2019918470

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…In [12], the authors are including an examination object detection method, object representation, and feature selection, and object tracking over many frames. By comparing seven different techniques (Kalman Filter [13], Particle Filte[14]r, Mean shift [7], CamShift [15], KLT tracker [16], template matching [17], Contour Tracking [18]) relied on number of object tracked, occlusion handling, optimal or not.…”

Section: Related Workmentioning

confidence: 99%

A WSM-based Comparative Study of Vision Tracking Methodologies

Bouraya¹,

Belangour²

2021

IJACSA

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Vision tracking is a key component of a video sequence. It is the process of locating single or multiple moving objects over time using one or many cameras. The latter's function consists of detecting, categorizing, and tracking. The development of the trustworthy solution for video sequence analysis opens up new horizons for a variety of applications, including intelligent transportation systems, biomedical, agriculture, human-machine interaction, augmented reality, video surveillance, robots, and many crucial research areas. To make efficient models, there are challenges in video observation to deal with, such as problems with the environment, light variation, pose variation, motion blur, clutter, occlusion, and so on. In this paper, we present several techniques that addressed the issues of detecting and tracking multiple targets on video sequences. The proposed comparative study relied on different methodologies. This paper's purpose is to list various approaches, classify them, and compare them, using the Weighted Scoring Model (WSM) comparison method. This includes studying these algorithms, selecting relevant comparison criteria, assigning weights for each criterion, and lastly computing scores. The obtained results of this study will reveal the strong and weak points of each algorithm mentioned and discussed.

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Section: Related Workmentioning

confidence: 99%

A WSM-based Comparative Study of Vision Tracking Methodologies

Bouraya¹,

Belangour²

2021

IJACSA

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“…Researchers used this method vastly in page-turning and score-following studies [32]. The accuracy of object detection function is highly dependent on video recording equipment, and any vibration of cameras or changing the distance to moving objects can affect results [43].…”

Section: Video Processing Limitationsmentioning

confidence: 99%

Automatic Page-Turner for Pianists With Wearable Motion Detector

Mirazimzadeh¹

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Turning book pages is a simple act that readers do while reading. This simple act that we take for granted could be challenging when both hands are engaged in other tasks. In case that a pianist sight-read, using one hand for turning the music sheets can cause distraction or short delay in their performance. Previous studies predominantly utilized audio or video processing techniques for following the musicians' performance. We suggested a framework that uses wearable technologies as an alternative solution for solving page-turning problems. The proposed design recognizes the pianist's wrists oscillatory motion with accelerometer sensors and tries to sync them to digital music notes. This system uses dynamic time warping and cross-correlation methods to find the optimum alignment between actual and captured data and reducing time latency. Eventually, a heuristic decision model assesses the pianist's performance and decides to turn the digital music sheet at predefined points.

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