標題: GMM與EM應用於路測雷達偵測器三車種學習演算法的研發
Develop Three Vehicles Classifier for Road-Side Radar Detector Using GMM and EM Method
作者: 卓訓榮
CHO HSUN-JUNG
國立交通大學運輸科技與管理學系(所)
關鍵字: 即時;車種分類;高斯混合模型;期望最大化演算法;Real-time;Vehicle classification;Gaussian mixed model;EM algorithm
公開日期: 2009
摘要: 本篇文章主要提出路側雷達偵測器(Road-side radar detector)應用於多車道的環境下所需要的車種分類器 (vehicle type classifier)。本研究以電壓訊號作為輸入變數,並進一步透過傅利葉轉換成頻譜訊號。利用車輛通過偵測區域時,所得到的特徵值,進行學習模型的樣本。 車種分類器的學習架構包含的模型與演算法,使路側雷達偵測器能依據現實的道路環境,偵測通過的車輛來獲得學習樣本,並即時得到通過的車輛是位於哪一個車道的車種資訊。其中,車種主要區成摩托車,小型車,大型車三類。分類所使用的統計模型為二維的高斯混合型,並利用期望最大化演算法(EM algorithm)求解模型參數。
Counting traffic in a single lane is a basic task that can be achieved by using traffic detectors to detect passing vehicles, but it is difficult for road-side radar system to simultaneously detect different vehicle types in multi-lane environments, since the signals reflected from passing vehicles in a single lane influence neighboring lanes. The spread of reflected signals created difficulty in accurately identifying lane boundaries, and leaded that a vehicle classifier in multi-lane situations is in the experimental stage. The aim of this research is to provide a real-time vehicle type classifier in multilane situations based on the lane boundary results. An on-line learning procedure is proposed to form a vehicle classifier. Such a vehicle classifier will utilize the results of on-line automatic lane boundary estimator, and distinguish the vehicles including motorcycles, small-sized and large-sized vehicle. GMM is applied to form a learning model, and integrates an EM algorithm to maximize the likelihood. The real-world data will be gathered to examine the performance of the vehicle classifier.
官方說明文件#: NSC98-2221-E009-104
URI: http://hdl.handle.net/11536/101406
https://www.grb.gov.tw/search/planDetail?id=1898671&docId=314422
Appears in Collections:Research Plans