标题: 基于Haar特征和HoG特征的行人侦测
Pedestrian Detection Based on Haar and HoG features
作者: 陈奕竹
Chen, Yi-Chu
林进灯
Lin, Chin-Teng
电控工程研究所
关键字: 行人侦测;Haar;HoG;pedestrian detection;gpu
公开日期: 2011
摘要: 行人位置侦测是行人影像监测系统的第一个步骤,此部分结果的正确性对于接下来的行人追踪、行人姿势辨别有高度的影响,此外系统是否需要前置准备时间,也影响到实际应用的可能性。本论文提出一个行人位置侦测演算法,可以在不事先建立背景模型的前提下,在环境中选取行人的位置,并且对其可能前进方向做预测,由于不需要限制背景环境,此初步的行人位置侦测,可使用非固定的摄影机来做侦测,不论室内或室外,都可使用以此演算法发展出的即时行人侦测系统。我们将演算法区分为两部分:第一个部分为行人位置侦测,第二个部分为行人前进方向预测。在行人位置侦测部分,我们使用一个双层的架构,第一层使用Haar特征训练出来的连结分类器,快速地挑选出画面中有可能为行人的位置,第二层再使用HoG特征训练出来的行人线性支持向量机,用更精细的方法,再次确认是否为行人,并将提取出来的HoG特征,做为下一阶段进行行人前进方向预测的资料。这种修改过的双层架构,结合了连结分类器运算时间快、可容忍的样本间差异较大等优点,对于短时间内扫瞄出画面中姿势各异的行人很有帮助,且其可支援紧密扫描,对于人群密度较高,有遮蔽现象发生时,也能达到高侦测率,而第一层架构上的错判问题,又可依靠第二层精细提取的HoG特征来做删减。下一个部分的行人前进方向预测,也使用第二层提取出的行人HoG特征来做判断,我们将行人姿势分别为正向、背向、往左和往右四个方向,由于使用线性支持向量机来做方向的判别,我们可以在很短的时间内,估测出行人可能的前进方向,此资讯可供作为更进一步的追踪系统的资讯,提供给使用者更有用的资讯。
此演算法有以下优点:(1)由于使用大量影像训练而成,影像含盖不相同的背景和光线条件,和一般以外形为基础的方法中,需要针对轮廓建立人形模型的缺点。(2)不需要使用连续的画面,只需要一张影像就能初步进行行人辨识处理。(3)对于行人外型变化容忍度高,身形、姿势、衣着和手持物都不易影响侦测结果。(4)演算法速度快,可应用于即时侦测系统或是移动的摄影机中。(5)实验结果里,最好的行人侦测系统的辨识率达到90.31%。若实现于即时的行人侦测系统上,侦测率达到87.43%。
In this thesis, we propose a new pedestrian detection algorithm without a pre-built background model, and use it to develop a real-time pedestrian detection system. The pedestrian detection algorithm can be functionally partitioned into two parts: pedestrian detection and pedestrian orientation estimation. In pedestrian detection, we use a two-stage structure to detect pedestrians in real-time. At the first stage, we use a cascade classifiers trained with Haar features to quickly select regions of interest in the frame. At the second stage, we further confirm each region of interest with a linear support vector machine trained with HoG (Histogram of oriented gradients) features. In the pedestrian orientation estimation part, the program will use the preserved HoG features to suggest the moving direction of each pedestrian inside an incompletely controlled environment. The second stage is implemented on graphic processing unit (GPU) using CUDA techniques. CUDA further increase the processing speed of the system.
This method could be used as a preprocessing stage of a tracking system or an on-board vision system for vehicles. And may help facility surveillance cameras located in building, entrances and on road with automatic detection capability.
We combined two kinds of features trained by different style classifiers to maintain the system with short processing time and high detection rate. In pedestrian recognition, we obtain different scale of pedestrian examples from online datasets. And manually categorize the samples in different orientations. We exploit the relation of two kinds of features and classifiers. Then, experiments are detailed set to extract the proper parameters for different situation.
The pedestrian detection method has the following advantages: (1) Learning from masses of example images could expand the inter objects flexibility and adapt different environment. (2)Pedestrian recognition and orientation estimation could be done in a single image. (3)The system can detect pedestrians in variety of postures, shapes and walking directions. (4) The processing speed is fast. This algorithm can be realized in real time. (5) From experiments, accuracy rate of recognition could achieve up to 90.31%. We implement this algorithm in a real-time system. The system can detect pedestrians in the scene in real time with 87.43% detection rate.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079812547
http://hdl.handle.net/11536/46904
显示于类别:Thesis