IJE TRANSACTIONS C: Aspects Vol. 28, No. 12 (December 2015) 1711-1719   

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A. Gh. Sorkhi, H. Hassanpour and P. L. Mazzeo
( Received: May 23, 2015 – Accepted: December 24, 2015 )

Abstract    Non-overlapping field-of-view (FOV) cameras are used in surveillance system to cover a wider area. Tracking in such systems is generally performed in two distinct steps. In the first step, people are identified and tracked in the FOV of a single camera. In the second step, re-identification of the people is carried out to track them in the whole area under surveillance. Various conventional features such as clothes and appearance of person have been used to identify peoples in the cameras. However, similarity between appearance and clothes of people causes unreliable results. Thus, much reliable features are still required to increase the ability of tracking system. The aim of this paper is to propose a novel algorithm to identify people in a network of cameras with disjoints views. In our proposed methodology, according to relative size of various parts, human body are divided into three portions of head, middle section and lower section. Considering histograms of these portions, matching process was done aiming to distinguish persons in FOV of cameras. The other main challenge in such multi-camera system is to deal with illumination changes and varying appearance of people at different cameras view. The cumulative brightness transform function (CBTF) was employed to alleviate the difficulty of illumination changes. Experiments were conducted in RGB, YCbCr and HSV color spaces and it was found that YCbCr color space gives a better performance result compared to other color spaces used in this work.


Keywords    People tracking, Camera networks, non-overlapping FOV, various segments of human body, Cumulative brightness transform function


چکیده    در سال­های اخیر، از شبکه­ای از دوربین­ها با دید مجزا، به منظور نظارت بر ناحیه وسیع استفاده شده است. ردیابی در این نوع از سیستم­ها، معمولا بر پایه دو مرحله اساسی می­باشد. در مرحله اول، افراد، در زاویه دید یک دوربین، شناسایی و ردیابی می­شوند. در مرحله دوم، دوباره شناسایی افراد به منظور ردیابی آنها در تمام ناحیه تحت نظارت انجام می­گیرد. در این نوع از سیستم­ها، ویژگی­های زیادی به مانند لباس و ظاهر افراد به منظور شناسایی استفاده شده است. ولی شباهت بین لباس و ظاهر افراد نتایج قابل قبولی را از خود نشان نداده است. به همین دلیل استفاده از ویژگی­های قابل اطمینان به منظور بالا بردن توانایی سیستم­های ردیابی همچنان مورد نیاز می­باشد. هدف از مقاله حاضر، ارائه الگوریتمی جدید به منظور شناسایی افراد در شبکه­ای از دوربین­ها با دید مجزا می­باشد. در روش پیشنهادی، بر اساس اندازه نسبی قسمت­های مختلف، بدن انسان به سه قسمت مجزا، سر، میان­تنه و پایین تنه تقسیم شده است. عمل شناسایی افراد با توجه به هیستوگرام قسمت­های مختلف بدن انسان در زاویه دید دوربین­ها انجام می­شود. یکی دیگر از چالش­های موجود در این نوع از سیستم­ها تغییر روشنایی و تغییر کردن ظاهر افراد در زاویه دید دوربین­های متفاوت می­باشد. تابع انتقال روشنایی تجمعی به منظور کاهش مشکلات تغییرات شدت روشنایی در این نوع از سیستم­ها استفاده شده است. آزمایشات در فضاهای رنگی RGB، YCbCr و HSV به منظور ردیابی و شناسایی مجدد افراد انجام شده است که فضای رنگی YCbCr عملکرد بهتر در مقایسه با دیگر فضا­های رنگی در این نوع از سیستم­ها از خود نشان داده است.



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