رديابي دقيق اشيای متحرک با استفاده از الگوريتم‌هاي Sift، KLT و DBSCAN
محورهای موضوعی : مهندسی برق و کامپیوتر
عزیز کرمیانی
1
(دانشگاه شهید مدنی آذربایجان)
عسگرعلی بویر
2
(دانشگاه شهید مدنی آذربایجان)
کلید واژه: اشیای متحرک ردیابی DBSCAN KLT SIFT,
چکیده مقاله :
کشف و رديابي اشيای متحرک گامی اساسي در تجزيه و تحليل ويدئو مي‌باشد. در اين مقاله روشي جديد را براي رديابي هم‌زمان چندين شيء متحرک در حوزه دید دوربین ثابت ارائه خواهيم کرد. در روش پيشنهادي مکان اشيای متحرک موجود در حوزه ديد دوربين را در هر مرحله و با استفاده از اطلاعات حرکت موجود بين دو فريم متوالي شامل فريم قبلي و فريم جاري از نظر زماني تعيين مي‌کنيم. در هر مرحله نقاط ویژگی Sift را روي فريم قبلي استخراج کرده و تناظر اين نقاط ويژگي را با استفاده از الگوريتم تناظريابي نقاط کلیدی KLT روي فريم جاري به دست مي‌آوريم. در ادامه و با در اختيار داشتن نقاط ويژگي متناظر بين دو فريم متوالي، اندازه حرکت نقاط ويژگي را محاسبه کرده و با حذف نقاط ويژگي با جابه‌جايي ثابت و يا ناچیز، نقاط ويژگي مرتبط به اشيای متحرک را کشف خواهيم کرد. سپس نقاط ويژگي برچسب‌گذاري شده به عنوان اشيای متحرک را با استفاده از الگوريتم خوشه‌بندي DBSCAN به خوشه‌هاي مختلف به عنوان اشيای متحرک دسته‌بندي مي‌کنيم. با اين روش و در هر لحظه مکان تمامي اشيای متحرک موجود در حوزه ديد دوربين به دست آمده که با تناظريابي يک به يک بين اين اشيا و اشيای به دست آمده در فريم قبلي مکان جديد هر شيء را تعيين مي‌کنيم. نتايج روش پيشنهادي حاکي از دقت بالا و زمان مصرفي قابل قبول براي رديابي اشيای متحرک مي‌باشد. روش پیشنهادی دارای دقت 95% برای ردیابی اشیای متحرک بوده و در هر ثانیه 33 فریم را پردازش می‌کند که در مقایسه با روش‌های معمول از نظر دقت و سرعت عملکرد مطلوبی دارد.
Detecting and tracking of moving objects is an important task in analyzing videos. In this paper, we propose a new method for tracking several concurrent moving objects of fixed camera. In the proposed method, at each stage, the location of moving objects in front of camera view is obtained information between two current and previous frames. In each step, Sift’s edge points is obtained based on previous frame and to get the correspondence of these feature points by the use of KLT feature point correspondence algorithm on the current frame. Then having correspondent feature points between two sequence frames, we would estimate the distance by eliminating partial or fixed moving feature points related to moving objects. The classification of labeled features as moving objects is done using DBSCAN clustering algorithm into different clusters. By this method and on each moment, the situation of all existing moving objects in camera view which has got by one by one correspondence between these objects, is determined. The obtained results of the proposed method shows a high degree of accuracy and acceptable consuming time to track moving objects.
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