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行人检测就是计算机对于给定的图像和视频,判断出其中是否有行人,如果有还需要给出行人的具体位置。行人检测是行人跟踪,行为分析,步态分析,行人身份识别等研究的基础和前提,一个好的行人检测算法能够为后者提供有力的支持和保障。文章选取机器学习支持向量机算法和深度学习框架caffe的Imagenet模型进行分析与实现,对两者的识别率进行对比,比较二者的测试性能,得出深度学习较机器学习的测试性能好的结论。
Pedestrian detection is the computer for a given image and video to determine whether there are pedestrians, if there is also need to give the specific location of pedestrians. Pedestrian detection is the basis and premise of pedestrian tracking, behavior analysis, gait analysis and pedestrian identification. A good pedestrian detection algorithm can provide strong support and guarantee for the latter. The article chooses the machine learning SVM algorithm and the imagenet model of caffe, which is a deep learning framework, to analyze and realize. The recognition rate of the two is compared and the test performance of the two is compared. The conclusion is drawn that the deep learning is better than the performance of machine learning .