توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱A Novel Vehicle Tracking Method with Occlusion Handling Using Longest Common Substring of Chain–Codes
نویسنده(ها): ،
اطلاعات انتشار: چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۶
Vehicle tracking is an essential requirement of any vision based Intelligent Transportation System for extracting different traffic parameters, efficiently. Handling inter–object occlusion is the most challenging part of tracking as a process of finding and following interested objects in a sequence of video frames. In this paper we present a system, based on code–book background model for motion segmentation and Kalman filter for tracking with a new approach for occlusion. This approach separates occluded vehicles based on longest common substring of chain codes. We use this tracking system to estimate some traffic parameters. Experimental results show the efficiency of the method<\div>

۲Moving Vehicle Tracking Using Disjoint View Multicameras
نویسنده(ها): ،
اطلاعات انتشار: Iranian Journal of Electrical and Electronic Engineering، هفتم،شماره۳، Sep ۲۰۱۱، سال
تعداد صفحات: ۱۱
Multicamera vehicle tracking is a necessary part of any video–based intelligent transportation system for extracting different traffic parameters; such as link travel times and origin\destination counts. In many applications, it is needed to locate traffic cameras disjoint from each other to cover a wide area. This paper presents a method for tracking moving vehicles in such camera networks. The proposed method introduces a new method for handling inter–object occlusions; as the most challenging part of the single camera tracking phase. This approach is based on coding the silhouette of moving objects before and after occlusion and separating occluded vehicles by computing the longest common substring of the related chain codes. In addition, to improve the accuracy of the tracking method in the multicamera phase, a new feature based on the relationships among surrounding vehicles is formulated. The proposed feature can efficiently improve the efficiency of the appearance (or space–time) features when they cannot discriminate between correspondent and non–correspondent vehicles due to noise or dynamic condition of traffic scenes. A graph–based approach is then used to track vehicles in the camera network. Experimental results show the efficiency of the proposed methods.
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