تاثیر اطلاعات بافتی و استخراج نقاط کلیدی در ردیابی بصری شی مبتنی بر تبدیل شباهت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری/ دانشکده مهندسی کامپیوتر دانشگاه یزد- بخش هوش مصنوعی

2 استادیار، دانشکده مهندسی کامپیوتر دانشگاه یزد - بخش هوش مصنوعی

چکیده

در سال‌های اخیر، ردیابی شی در محیط‌های مختلف با اشیا متنوع، اهمیت قابل توجهی یافته است. یک ویژگی بسیار مهم، ردیابی سریع، بدون نیاز به سخت‌افزار خاص و پیش‌آموزش است. ردیاب‌های مبتنی بر فیلتر همبستگی متمایزکننده، نتایج مثبتی را از نظر سرعت و دقت ارائه داده‌اند. اگرچه در بسیاری از این ردیاب‌ها، موقعیت شی در هر فریم بر اساس انتقال و مقیاس‌های هرمی تخمین زده می‌شود، در الگوریتم تبدیل شباهت، انتقال، مقیاس و چرخش برای یافتن موقعیت شی برآورد می‌شوند. در این الگوریتم، ویژگی هیستوگرام گرادیا‌ن‌های جهت‌دار استخراج شده است. در مقاله‌ی پیش‌رو، دو رویکرد متفاوت جهت استخراج ویژگی در این الگوریتم اتخاذ شده است. روش اول از تصاویر مقیاس‌بندی شده با استفاده از محدود‌سازی حداقل واریانس استفاده می‌کند. سپس با روش تغییر مقیاس در ماتریس‌های هم‌رخداد، ویژگی‌ها به سطح دیگری نگاشت می‌شوند. رویکرد دوم، ترکیبی از ویژگی‌های الگوی دودویی محلی رنگی متضاد و ویژگی مقاوم تسریع یافته ارائه می‌دهد. مجموعه‌ داده‌ی مورد ارزیابی OTB-2015 شامل 100 دنباله‌ی ویدیویی است. هر دو رویکرد، نتایج کلی مقاله‌ی پایه را تا حدود 3 درصد بهبود داده‌اند. روش اول در چالش رزولوشن پایین تا 7 درصد و روش دوم در چالش چرخش تا 4 درصد، نتایج را افزایش داده‌اند.

کلیدواژه‌ها

عنوان مقاله [English]

The Effects of Textural Information and Key Points Extraction on Visual Object Tracking, Based on Similarity Transformation

نویسندگان [English]

  • Solmaz Abbasi 1
  • Mehdi Rezaeian 2
1 Ph.D Candidate, Computer Engineering Department, Yazd University, Yazd. ّIran
2 Associate Professor, Computer Engineering Department, Yazd University, Yazd. Iran.
چکیده [English]

Visual object tracking in arbitrary environments with arbitrary objects has gained considerable importance in recent years. A very significant feature, which makes a tracker useful, is real time tracking without needing GPU and pre-train algorithms. In the recent decade, the trackers, which function on the basis of discriminative correlation filters, have promised positive results in terms of both speed and accuracy. Although, in most of such methods, the estimation of the position of the object in each frame is computed based on transformation and pyramid scales, in Large Displacement Estimation of Similarity transformation algorithm, translation, scale and rotation are estimated in each frame. In this paper, the Histogram of Oriented Gradient is considered as feature extraction. Here, we adopt two different approaches. The first approach uses scaled images as a feature matrix by applying minimum variance quantization. The second approach, uses a combination of opposite color local binary patterns and Speeded-Up Robust Features. By using these two methods, we are able to extract helpful and fast features, and therefore improve the results of tracking against challenging attributes. The OTB-2015 dataset is utilized for evaluating tracker. The results show precision of trackers improve 3%. Additionally, the first tracker increase the result about 7% against low resolution and the second one can be helpful about 4% in facing rotation challenge.    

کلیدواژه‌ها [English]

  • Visual Object Tracking
  • Similarity Transformation
  • Minimum Variance Quantization
  • Opposite Color Local Binary Patterns
  • Speeded-Up Robust Features

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پژوهش های نظری و کاربردی هوش ماشینی
دوره 1، شماره 1
فروردین 1402
صفحه 60-72

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