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项目实战—行人检测之SVM实战

本次我们将使用SVM+HOG来进行行人识别。行人检测，就是将一张图片中的行人检测出来，并输出bounding box级别的结果。而如果将各个行人之间的轨迹关联起来，就变成了行人跟踪。而行人检索则是把一段视频中的某个感兴趣的人检索出来。

在2005年CVPR上，来自法国的研究人员Navneet Dalal和Bill Triggs提出利用Hog进行特征提取，利用线性SVM作为分类器，从而实现行人检测。

HOG+SVM行人检测

首先计算样本图像的HOG描述子，组成一个特征向量矩阵，对应的要有一个指定每个特征向量的类别的类标向量，输入SVM中进行训练，训练好的SVM分类器保存为XML文件，
然后根据其中的支持向量和参数生成OpenCV中的HOG描述子可用的检测子参数，再调用OpenCV中的多尺度检测函数进行行人检测。

现在利用HOG特征来进行行人检测，既然要有了特征，现在其实要有一个方法来判断是否一个图片的某一部分是行人，SVM是一个很好的机器学习方法，可以用来分类，结合HOG特征就可以用来检测图片中的行人。OpenCV中集成了一个方法，getDefaultPeopleDetector等可以直接得到一个SVM的分类器，这个分类器是OpenCV自带的已经训练好的，可以直接拿来使用。下面可以看一下使用它的代码。

首先我们进行图片检测：

# import the necessary packages from __future__ import print_function from imutils.object_detection import non_max_suppression from imutils import paths import numpy as np import argparse import imutils import cv2 # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-i", "--images", required=True, help="path to images directory") args = vars(ap.parse_args()) # initialize the HOG descriptor/person detector hog = cv2.HOGDescriptor() hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) # loop over the image paths for imagePath in paths.list_images(args["images"]): # load the image and resize it to (1) reduce detection time # and (2) improve detection accuracy image = cv2.imread(imagePath) image = imutils.resize(image, width=min(400, image.shape[1])) orig = image.copy() # detect people in the image (rects, weights) = hog.detectMultiScale(image, winStride=(1, 1), padding=(8, 8), scale=1.05) # draw the original bounding boxes for (x, y, w, h) in rects: cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2) # apply non-maxima suppression to the bounding boxes using a # fairly large overlap threshold to try to maintain overlapping # boxes that are still people rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects]) pick = non_max_suppression(rects, probs=None, overlapThresh=0.45) # draw the final bounding boxes for (xA, yA, xB, yB) in pick: cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2) # show some information on the number of bounding boxes filename = imagePath[imagePath.rfind("/") + 1:] print("[INFO] {}: {} original boxes, {} after suppression".format( filename, len(rects), len(pick))) # show the output images cv2.imshow("Before NMS", orig) cv2.moveWindow('Before NMS', x=0, y=0) cv2.imshow("After NMS", image) cv2.moveWindow('After NMS', x=orig.shape[1], y=0) k=cv2.waitKey(0) if k==ord('q'): break

我们加入了非极大值抑制（前面已经讲过），用来去除一些其他的噪声信息。

实验结果：

右边为使用了极大值抑制之后的结果。

现在我们可以进行视频检测，来看代码：

# import the necessary packages from __future__ import print_function from imutils.object_detection import non_max_suppression from imutils import paths import numpy as np import argparse import imutils import cv2 import time # construct the argument parse and parse the arguments # ap = argparse.ArgumentParser() # ap.add_argument("-i", "--images", required=True, help="path to images directory") # args = vars(ap.parse_args()) # initialize the HOG descriptor/person detector hog = cv2.HOGDescriptor() hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) # cap = cv2.VideoCapture('videos/ 礼让斑马线！齐齐哈尔城市文明的伤！ .mp4') # cap = cv2.VideoCapture('../../data/TownCentreXVID.mp4') fps = cap.get(cv2.CAP_PROP_FPS) # 25.0 print("Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}".format(fps)) num_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT) print(' 共有 ', num_frames, ' 帧 ') # 共有 2499.0 帧 frame_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) frame_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) print(' 高： ', frame_height, ' 宽： ', frame_width) # 高： 480.0 宽： 640.0 # exit(0) # 跳过多少帧 skips = 20 # loop over the image paths # for imagePath in paths.list_images(args["images"]): while cap.isOpened(): # load the image and resize it to (1) reduce detection time # and (2) improve detection accuracy # image = cv2.imread(imagePath) ret, frame = cap.read() image = frame # current = cap.get(cv2.CAP_PROP_POS_FRAMES) if current % skips != 0: continue image = imutils.resize(image, width=min(400, image.shape[1])) orig = image.copy() # detect people in the image (rects, weights) = hog.detectMultiScale(image, winStride=(4, 4), padding=(8, 8), scale=1.05) # draw the original bounding boxes for (x, y, w, h) in rects: cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2) # apply non-maxima suppression to the bounding boxes using a # fairly large overlap threshold to try to maintain overlapping # boxes that are still people rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects]) pick = non_max_suppression(rects, probs=None, overlapThresh=0.65) # draw the final bounding boxes for (xA, yA, xB, yB) in pick: cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2) # show some information on the number of bounding boxes # filename = imagePath[imagePath.rfind("/") + 1:] # print("[INFO] {}: {} original boxes, {} after suppression".format( print("[INFO] {} original boxes, {} after suppression".format(len(rects), len(pick))) # show the output images cv2.imshow("Before NMS", orig) cv2.imshow("After NMS", image) cv2.moveWindow("After NMS", y=0, x=400) key = cv2.waitKey(delay=50) if key == ord("q"): break # When everything done, release the capture cap.release() cv2.destroyAllWindows()

这是视频中的一部分截图，我们可以看到结果。

大家请自行选择视频进行试验。