توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱A Simple Approach for Real Time Speed Estimation of On Road Vehicles Using Video Sequences
نویسنده(ها): ،
اطلاعات انتشار: سومین کنفرانس ملی و اولین کنفرانس بین المللی پژوهش هایی کاربردی در مهندسی برق، مکانیک و مکاترونیک، سال
تعداد صفحات: ۷
This paper provides an efficient and simple approach towards real–time speed estimation of on road vehicles for surveillance applications. In the presented method, videos are supposed to be captured with a stationary camera mounted on a two–lane road and there is no need for the camera to be calibrated. The algorithm has two main phases, in the first phase there is an interactive procedure in which lane borders and real world distances are defined just once at the beginning. Then, based on the already received information, two rectangular ROIs are defined for each lane. In the second phase, approximate binary mask of the foreground is created differencing the two consecutive frames. Eventually, calculating centroids and the norm values of the binary mask in the ROIs, algorithm can compute the time that it takes each vehicle to pass between the two aforementioned lines and thus, average speed can be computed. In short, although the algorithm of this paper is simple, it is real–time and fficient, and its implementation doesn’t require any specific hardware. The average error of speed estimation is ±3km\h and the detection accuracy is 83 %.<\div>

۲A SVM Based Approach for Real Time Detection and Classification of Vehicles at the Toll Gates Using Video Sequences
نویسنده(ها): ،
اطلاعات انتشار: سومین کنفرانس ملی و اولین کنفرانس بین المللی پژوهش هایی کاربردی در مهندسی برق، مکانیک و مکاترونیک، سال
تعداد صفحات: ۸
This paper aims to present a real–time scheme for detection and classification of vehicles passing the toll gates in Iran. In our approach, a set of videos are captured using a stationary camera, placed on the roadside, at a little distance from the toll booth. The algorithm is designed in a way that there is no need for camera calibration. Based on our videos, 3 ROIs are defined, two of them are considered to determine if a vehicle is passing and the other one is the region containing the vehicle. This work starts with the training phase, in which, for each image in a manually gathered database, HOG vectors are extracted. Two SVMs are trained in this phase, one for distinguishing vehicles from non–vehicles, and one for classifying vehicles into light and heavy vehicles. After finishing the training, in the testing phase, firstly, foreground mask is obtained differencing two consecutive frames of the video. Then, those two aforementioned ROIs are checked in every frame and as soon as a vehicle is inside the interest region, that ROI is captured. Next, the captured frame is passed to the first SVM and it is classified as vehicle or non–vehicle. Those which are identified as vehicles are passed to the second SVM to be classified as light or heavy vehicle. Average true–positive and precision rates of the vehicle detection step are 92.5% and 97.5% respectively and the same rates, for the recognition step, are 98% and 0.99%.<\div>
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