運用AdaBoost車輛檢測之特性和校正的事件分類

Abstract

近年來，為了提高分析大量影像資料的效率與準確度，影像式的車輛偵測技術廣泛應用在智慧型運輸系統中，且相關研究以及應用也愈來愈多並且受到重視。在本研究中，我們利用AdaBoost演算法的高偵測率和高斯混和模型結合我們所提出的校正方法去做分類。我們會將原始的彩色圖像轉化成灰階圖像並且為了加速，會去調整影像的大小。經過調整過後的影像會送到高斯混和模型，藉此去建立可靠的背景圖像，並且使用減背景的技術去結取出前景物體。在這個階段，我們使用校正去做座標轉換，也就是從像素座標轉換到世界座標，並且結合利用由高斯混和模型所結取出來的前景去進一步記錄前景的資訊，像是前景在世界座標的寬度和長度，以便接下去做分類。在這裡，我們使用重疊面積的概念去做追蹤和記錄前景的資訊。對於分類的準則，我們使用 AdaBoost 車輛檢測器去偵測和驗證藉由高斯混和模型所結取出的前景是否是車子。藉由AdaBoost車輛檢測器，我們可以將車輛和行人、摩托車、散落物做區分。接下來，我們利用高度和寬度的比例、透過校正所得的速度以及邊緣複雜度去區分行人和散落物。因為我們發現行人和散落物有不同的邊緣複雜度範圍。 本研究的目的是透過校正前景的寬度、高度、和速度，並且利用AdaBoost車輛檢測器去進行車輛、行人、散落物的分類。因此，該系統可以警告我們任何危險的情況以做到保護司機和行人的安全。實驗結果證明，我們所提出的系統可以達到良好的性能以去分類車輛、行人、散落物。此實施的系統也提取了有用的交通資訊可以做進一步的發展，像是車種的分類:轎車、卡車、客運;或是行人和機車的分類。

Visual-based vehicle detection techniques applied to Intelligent Transportation System (ITS) to improve the efficiency and precision of analyzing heavy video information have been studied for years. In this study, we take advantage of AdaBoost algorithm’s accurate detection rate cooperate with Gaussian Mixture Model (GMM) and calibration method we proposed to do classification. We transform the raw color image into a gray level image and resize it for acceleration. After resizing, the image is send to Gaussian mixture model (GMM) approach to obtain reliable background images, and then use background subtraction technique to extract foreground objects. In this stage, we use calibration for transferring pixel coordinates to world coordinates and cooperate with foreground subtracted by GMM to get and record foreground’s information, such as width and height for further classification. Here, we use concept of overlapping area for tracking and recording foreground’s information. For classification’s criteria, the AdaBoost vehicle detector uses slicing window to detect vehicles and verifies whether the foreground subtracted by GMM [22] [23] is vehicle. Through AdaBoost vehicle detector, we can separate vehicles from pedestrians, motorcycles, and objects. Next, we utilize width and height ratio, edge complexity, and speed get from calibration to separate pedestrians and motorcycles from objects. Because we found that pedestrians and objects have different edge complexity range. The objective of this study is to classify vehicles, pedestrians, and objects which include AdaBoost vehicle detector and calibrating the foreground’s width, height, and speed. Therefore, the system can warns us of the dangerous situation to protect both drivers and pedestrians. The experimental results proved the proposed system achieved a good performance of classifying vehicles, pedestrians, and objects. The implemented system also extracted useful traffic information that can be used for further processing, like classifying vehicles, ex: sedan, truck, bus, and classifying pedestrians and motorcycles by adding auxiliary features.