前言

通过阅读相关文献及测试，找到了一种基于多模板匹配的改进方法，可以对遥感视频卫星中的移动目标进行探测，并绘制其轨迹。根据实验结果发现，可以比较有效的对运动目标进行跟踪。

一、原理

核心思想比较简单。即通过不同旋转角度的模板同时匹配，在多个结果中，找到相似度最大的结果，即认为匹配成功。 在视频的某一帧将这些模板分别进行匹配，即可获得较为准确的结果。

某一帧的物体搜索窗口如上图所示。0°表示提取的原始模板，将原始模板以8个方向进行旋转，可得到8个不同旋转角度的模板。 依次与窗口进行模板匹配，可以得到相似度。取相似度最大的模板对应的坐标结果作为轨迹。

同时根据不同的精度需求，可以有4模板、8模板和16模板，对应方向如下。模板数目越多，其对旋转的检测性就越好、越精确。但同时计算量也会成倍增加。

二、代码实现

# coding=utf-8
import cv2
import numpy as np
import mathdef calcVelocity(x1, x2, y1, y2, res, wT):dist = pow(pow(y1 - y2, 2) + pow(x1 - x2, 2), 0.5) * resv = dist / (wT / 1000.0) * 3.6return v# ---------------必要参数---------------
# 待识别视频路径
video_path = 'E:\\object\\test_real.mp4'
# 卫星视频地表分辨率
resolution = 2
# 估计最快运动速度
velocity = 850
# ---------------必要参数---------------# ---------------可选参数---------------
# 提取的模板是否为正方形
isSquare = True
# 是否自动根据速度信息计算阈值
isAutoDisThresh = True
# 是否为多模板
isMultiTemplate = True
# 是否采用均值对轨迹进行平滑
isSmooth = True
# 相邻轨迹点之间的距离阈值
dis_thresh = 10
# 多模板个数
templateNum = 8
# 初始待选窗口大小半径
range_d = 30
# 灰度阈值敏感度，越大灰度阈值越低
gray_factor = 0.2
# 识别框缩放因子，越大绘制的识别框越大
scale_factor = 1.5
# 模板缩放因子，越大模板图像越大
template_factor = 0.6
# 识别框颜色
color = (0, 0, 255)
# 输出路径
parent_path = video_path.replace(video_path.split("\\")[-1], '')
out_path = parent_path + "object.avi"
out_path2 = parent_path + "track.avi"
out_path3 = parent_path + "points.txt"
out_path4 = parent_path + "velocity.txt"
out_path5 = parent_path + "template.jpg"
# ---------------可选参数---------------# 循环变量
count = 0# 打开视频
cap = cv2.VideoCapture(video_path)
cap2 = cv2.VideoCapture(video_path)
# 获取视频图像大小
# video_h对应竖直方向，video_w对应水平方向
video_h = int(cap.get(4))
video_w = int(cap.get(3))
total = int(cap.get(7))# 新建一张与视频等大的影像用于绘制轨迹
track = np.zeros((video_h, video_w, 3), np.uint8)# tlp用于存放待选窗口的左上角点
tlp = []
# rbp用于存放待选窗口的右下角点
rbp = []
# bottom_right_points用于存放目标区域的右下角点
bottom_right_points = []
# center_points用于存放目标区域的中心点
center_points = []
# trackPoints用于存放目标区域的左上角点
trackPoints = []
# Vs用于存放目标各帧速度
Vs = []# 根据视频信息计算每一帧的等待时间
if cap.get(5) != 0:waitTime = int(1000.0 / cap.get(5))fps = cap.get(5)# 如果为真，则自动确定距离阈值
if isAutoDisThresh:# 计算物体帧间最大运动范围(像素)max_range = math.ceil((5.0 * velocity) / (18.0 * resolution * (fps - 1)))# 计算最大移动距离，作为阈值dis_thresh = math.ceil(pow(pow(max_range, 2) + pow(max_range, 2), 0.5))fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(out_path, fourcc, fps, (video_w, video_h))
out2 = cv2.VideoWriter(out_path2, fourcc, fps, (video_w, video_h))# 首先提取模板图像
if cap2.isOpened():# 读取前两帧ret, frame1 = cap2.read()ret, frame2 = cap2.read()# 相减做差sub = cv2.subtract(frame1, frame2)# 得到的结果灰度化gray = cv2.cvtColor(sub, cv2.COLOR_BGR2GRAY)# 判断作差后的结果是否全为0if gray.max() != 0:# 找到最大值位置loc = np.where(gray == gray.max())loc_x = loc[1][0]loc_y = loc[0][0]# 以loc为中心，range_d为距离向外拓展得到windowwin_tl_x = loc_x - range_dwin_tl_y = loc_y - range_dwin_rb_x = loc_x + range_dwin_rb_y = loc_y + range_d# 一些越界的判断if win_tl_x < 0:win_tl_x = 0if win_tl_y < 0:win_tl_y = 0if win_rb_x > video_w:win_rb_x = video_wif win_rb_y > video_h:win_rb_y = video_h# 根据窗口坐标提取窗口内容win_ini = cv2.cvtColor(frame1[win_tl_y:win_rb_y, win_tl_x:win_rb_x, :], cv2.COLOR_BGR2GRAY)# 获取最大值位置对应的灰度值tem_img = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)# 由最大值对应灰度值计算合适的灰度阈值gray_thresh = tem_img[loc_y, loc_x] - gray_factor * tem_img[loc_y, loc_x]# 初始窗口二值化处理ret, thresh = cv2.threshold(win_ini, gray_thresh, 255, cv2.THRESH_BINARY)# 在初始窗口中寻找轮廓img2, contours, hi = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)# 有可能找到多个轮廓，但认为包含点数最多的那个轮廓是要找的轮廓length = []for item in contours:length.append(item.shape[0])target_contour = contours[length.index(max(length))]# 获取目标轮廓的坐标信息x, y, w, h = cv2.boundingRect(target_contour)if isSquare:# 保证提取的模板为正方形tem_tl_x = win_tl_x + xtem_tl_y = win_tl_y + ytem_rb_x = win_tl_x + x + wtem_rb_y = win_tl_y + y + hcenter_x = (tem_tl_x + tem_rb_x) / 2center_y = (tem_tl_y + tem_rb_y) / 2delta = int(template_factor * max(w, h))real_tl_x = center_x - deltareal_rb_x = center_x + deltareal_tl_y = center_y - deltareal_rb_y = center_y + deltaelse:# 不保证模板为正方形real_tl_x = win_tl_x + xreal_tl_y = win_tl_y + yreal_rb_x = win_tl_x + x + wreal_rb_y = win_tl_y + y + h# 一些越界判断if real_tl_x < 0:real_tl_x = 0if real_tl_y < 0:real_tl_y = 0if real_rb_x > video_w:real_rb_x = video_wif real_rb_y > video_h:real_rb_y = video_h# 提取模板内容template = frame1[real_tl_y:real_rb_y, real_tl_x:real_rb_x, :]# 获取模板的宽高，h竖直方向，w水平方向h = template.shape[0]w = template.shape[1]d = max(w, h)# 是否是多模板匹配if isMultiTemplate:if templateNum == 16:M22_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -22.5, 1)M45 = cv2.getRotationMatrix2D((d / 2, d / 2), -45, 1)M67_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -67.5, 1)M90 = cv2.getRotationMatrix2D((d / 2, d / 2), -90, 1)M112_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -112.5, 1)M135 = cv2.getRotationMatrix2D((d / 2, d / 2), -135, 1)M157_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -157.5, 1)M180 = cv2.getRotationMatrix2D((d / 2, d / 2), -180, 1)M202_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -202.5, 1)M225 = cv2.getRotationMatrix2D((d / 2, d / 2), -225, 1)M247_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -247.5, 1)M270 = cv2.getRotationMatrix2D((d / 2, d / 2), -270, 1)M292_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -292.5, 1)M315 = cv2.getRotationMatrix2D((d / 2, d / 2), -315, 1)M337_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -337.5, 1)template22_5 = cv2.warpAffine(template, M22_5, (d, d))template45 = cv2.warpAffine(template, M45, (d, d))template67_5 = cv2.warpAffine(template, M67_5, (d, d))template90 = cv2.warpAffine(template, M90, (d, d))template112_5 = cv2.warpAffine(template, M112_5, (d, d))template135 = cv2.warpAffine(template, M135, (d, d))template157_5 = cv2.warpAffine(template, M157_5, (d, d))template180 = cv2.warpAffine(template, M180, (d, d))template202_5 = cv2.warpAffine(template, M202_5, (d, d))template225 = cv2.warpAffine(template, M225, (d, d))template247_5 = cv2.warpAffine(template, M247_5, (d, d))template270 = cv2.warpAffine(template, M270, (d, d))template292_5 = cv2.warpAffine(template, M292_5, (d, d))template315 = cv2.warpAffine(template, M315, (d, d))template337_5 = cv2.warpAffine(template, M337_5, (d, d))elif templateNum == 8:M45 = cv2.getRotationMatrix2D((d / 2, d / 2), -45, 1)M90 = cv2.getRotationMatrix2D((d / 2, d / 2), -90, 1)M135 = cv2.getRotationMatrix2D((d / 2, d / 2), -135, 1)M180 = cv2.getRotationMatrix2D((d / 2, d / 2), -180, 1)M225 = cv2.getRotationMatrix2D((d / 2, d / 2), -225, 1)M270 = cv2.getRotationMatrix2D((d / 2, d / 2), -270, 1)M315 = cv2.getRotationMatrix2D((d / 2, d / 2), -315, 1)template45 = cv2.warpAffine(template, M45, (d, d))template90 = cv2.warpAffine(template, M90, (d, d))template135 = cv2.warpAffine(template, M135, (d, d))template180 = cv2.warpAffine(template, M180, (d, d))template225 = cv2.warpAffine(template, M225, (d, d))template270 = cv2.warpAffine(template, M270, (d, d))template315 = cv2.warpAffine(template, M315, (d, d))elif templateNum == 4:M90 = cv2.getRotationMatrix2D((d / 2, d / 2), -90, 1)M180 = cv2.getRotationMatrix2D((d / 2, d / 2), -180, 1)M270 = cv2.getRotationMatrix2D((d / 2, d / 2), -270, 1)template90 = cv2.warpAffine(template, M90, (d, d))template180 = cv2.warpAffine(template, M180, (d, d))template270 = cv2.warpAffine(template, M270, (d, d))cv2.imshow("Template", template)cv2.imwrite(out_path5, template)offset = int(scale_factor * d)# 计算待选窗口左上角点坐标tlx = loc_x - dtly = loc_y - d# 判断是否越界，越界则设置为0if tlx < 0:tlx = 0if tly < 0:tly = 0range_tl = (tlx, tly)# 计算待选窗口右下角点坐标rbx = loc_x + w + drby = loc_y + h + d# 判断是否越界，越界设置为视频长宽最大值if rbx > video_w:rbx = video_wif rby > video_h:rby = video_hrange_rb = (rbx, rby)# 放入角点坐标列表tlp.append(range_tl)rbp.append(range_rb)cap2.release()# 然后进行模板匹配
while cap.isOpened():# 读取每帧内容ret, frame = cap.read()# 判断帧内容是否为空，不为空继续if frame is None:breakelse:# 是否为多模板匹配模式if isMultiTemplate:if templateNum == 16:# 逐个模板进行匹配res = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :], template,cv2.TM_CCOEFF_NORMED)res22_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template22_5,cv2.TM_CCOEFF_NORMED)res67_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template67_5,cv2.TM_CCOEFF_NORMED)res112_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template112_5,cv2.TM_CCOEFF_NORMED)res157_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template157_5,cv2.TM_CCOEFF_NORMED)res202_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template202_5,cv2.TM_CCOEFF_NORMED)res247_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template247_5,cv2.TM_CCOEFF_NORMED)res292_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template292_5,cv2.TM_CCOEFF_NORMED)res337_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template337_5,cv2.TM_CCOEFF_NORMED)res90 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template90,cv2.TM_CCOEFF_NORMED)res180 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template180,cv2.TM_CCOEFF_NORMED)res270 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template270,cv2.TM_CCOEFF_NORMED)res45 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template45,cv2.TM_CCOEFF_NORMED)res135 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template135,cv2.TM_CCOEFF_NORMED)res225 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template225,cv2.TM_CCOEFF_NORMED)res315 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template315,cv2.TM_CCOEFF_NORMED)# 获取各模板对应的最大值m22_5 = np.max(res22_5)m67_5 = np.max(res67_5)m112_5 = np.max(res112_5)m157_5 = np.max(res157_5)m202_5 = np.max(res202_5)m247_5 = np.max(res247_5)m292_5 = np.max(res292_5)m337_5 = np.max(res337_5)m45 = np.max(res45)m135 = np.max(res135)m225 = np.max(res225)m315 = np.max(res315)m0 = np.max(res)m90 = np.max(res90)m180 = np.max(res180)m270 = np.max(res270)# 寻找最佳匹配结果m = max(m0, m22_5, m45, m67_5, m90,m112_5, m135, m157_5, m180,m202_5, m225, m247_5, m270,m292_5, m315, m337_5)# 获取最佳匹配结果对应的坐标信息if m == m0:mIndex = 0min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)elif m == m90:mIndex = 90min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res90)elif m == m180:mIndex = 180min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res180)elif m == m270:mIndex = 270min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res270)elif m == m45:mIndex = 45min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res45)elif m == m135:mIndex = 135min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res135)elif m == m225:mIndex = 225min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res225)elif m == m315:mIndex = 315min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res315)elif m == m22_5:mIndex = 22.5min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res22_5)elif m == m67_5:mIndex = 67.5min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res67_5)elif m == m112_5:mIndex = 112.5min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res112_5)elif m == m157_5:mIndex = 157.5min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res157_5)elif m == m202_5:mIndex = 202.5min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res202_5)elif m == m247_5:mIndex = 247.5min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res247_5)elif m == m292_5:mIndex = 292.5min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res292_5)elif m == m337_5:mIndex = 337.5min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res337_5)elif templateNum == 8:res = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :], template,cv2.TM_CCOEFF_NORMED)res90 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template90,cv2.TM_CCOEFF_NORMED)res180 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template180,cv2.TM_CCOEFF_NORMED)res270 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template270,cv2.TM_CCOEFF_NORMED)res45 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template45,cv2.TM_CCOEFF_NORMED)res135 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template135,cv2.TM_CCOEFF_NORMED)res225 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template225,cv2.TM_CCOEFF_NORMED)res315 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template315,cv2.TM_CCOEFF_NORMED)m45 = np.max(res45)m135 = np.max(res135)m225 = np.max(res225)m315 = np.max(res315)m0 = np.max(res)m90 = np.max(res90)m180 = np.max(res180)m270 = np.max(res270)m = max(m0, m45, m90, m135, m180, m225, m270, m315)if m == m0:mIndex = 0min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)elif m == m90:mIndex = 90min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res90)elif m == m180:mIndex = 180min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res180)elif m == m270:mIndex = 270min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res270)elif m == m45:mIndex = 45min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res45)elif m == m135:mIndex = 135min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res135)elif m == m225:mIndex = 225min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res225)elif m == m315:mIndex = 315min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res315)elif templateNum == 4:res = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :], template,cv2.TM_CCOEFF_NORMED)res90 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template90,cv2.TM_CCOEFF_NORMED)res180 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template180,cv2.TM_CCOEFF_NORMED)res270 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],template270,cv2.TM_CCOEFF_NORMED)m0 = np.max(res)m90 = np.max(res90)m180 = np.max(res180)m270 = np.max(res270)m = max(m0, m90, m180, m270)if m == m0:mIndex = 0min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)elif m == m90:mIndex = 90min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res90)elif m == m180:mIndex = 180min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res180)elif m == m270:mIndex = 270min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res270)else:res = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :], template,cv2.TM_CCOEFF_NORMED)min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)window = frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :]cv2.imshow("Window", window)# top_left坐标顺序(水平，竖直)(→，↓)top_left = (max_loc[0] + tlp[count][0], max_loc[1] + tlp[count][1])bottom_right = (top_left[0] + w, top_left[1] + h)center_point = ((top_left[0] + bottom_right[0]) / 2, (top_left[1] + bottom_right[1]) / 2)if trackPoints.__len__() == 0:# 计算待选窗口左上角点坐标tlx = top_left[0] - dtly = top_left[1] - d# 判断是否越界，越界则设置为0if tlx < 0:tlx = 0if tly < 0:tly = 0range_tl = (tlx, tly)# 计算待选窗口右下角点坐标rbx = top_left[0] + w + drby = top_left[1] + h + d# 判断是否越界，越界设置为视频长宽最大值if rbx > video_w:rbx = video_wif rby > video_h:rby = video_hrange_rb = (rbx, rby)# 将待选窗口左上角点坐标和右下角点坐标依次添加到列表中tlp.append(range_tl)rbp.append(range_rb)# 将目标区域的左上角点、中心点、右下角点坐标依次加入列表trackPoints.append(top_left)bottom_right_points.append(bottom_right)center_points.append(center_point)cv2.circle(track, center_point, 2, (0, 0, 255), -1)else:# 加入运动连续性约束，若相邻轨迹点距离相差大于阈值，则认为错误distance = abs(trackPoints[-1][0] - top_left[0]) + abs(trackPoints[-1][1] - top_left[1])if distance > dis_thresh:print '100%'breakelse:# 计算待选窗口左上角点坐标tlx = top_left[0] - dtly = top_left[1] - d# 判断是否越界，越界则设置为0if tlx < 0:tlx = 0if tly < 0:tly = 0range_tl = (tlx, tly)# 计算待选窗口右下角点坐标rbx = top_left[0] + w + drby = top_left[1] + h + d# 判断是否越界，越界设置为视频长宽最大值if rbx > video_w:rbx = video_wif rby > video_h:rby = video_hrange_rb = (rbx, rby)# 将待选窗口左上角点坐标和右下角点坐标依次添加到列表中tlp.append(range_tl)rbp.append(range_rb)# 将目标区域的左上角点、中心点、右下角点坐标依次加入列表trackPoints.append(top_left)bottom_right_points.append(bottom_right)# 判断是否采用均值平滑if isSmooth:# 采用均值平滑，平滑轨迹center_point = ((center_point[0] + center_points[-1][0]) / 2,(center_point[1] + center_points[-1][1]) / 2)center_points.append(center_point)# 绘制目标识别框cv2.rectangle(frame,(center_point[0] - offset, center_point[1] - offset),(center_point[0] + offset, center_point[1] + offset),color, 2)# 绘制运动轨迹cv2.line(track, center_points[-2], center_points[-1], (255, 255, 255), 1)# 计算速度Vs.append(calcVelocity(center_points[-2][0],center_points[-1][0],center_points[-2][1],center_points[-1][1],resolution,waitTime))# 输出目标、轨迹视频out.write(frame)out2.write(track)count += 1print round((count * 1.0 / total) * 100, 2), '%'# 显示结果cv2.imshow("Tr", track)cv2.imshow("Fr", frame)# 退出控制k = cv2.waitKey(waitTime) & 0xFFif k == 27:break# 打印轨迹坐标
print trackPointsprint '相邻帧距离阈值:', dis_thresh
print '灰度阈值:', gray_thresh
print '模板缩放因子:', template_factor
print '识别框缩放因子:', scale_factor# 输出中心点轨迹
output = open(out_path3, 'w')
for item in center_points:output.write(item.__str__() + "\n")# 输出各帧速度
output2 = open(out_path4, 'w')
for item in Vs:output2.write(item.__str__() + "\n")# 释放对象
cap.release()
out.release()
out2.release()
output.close()
output2.close()

在代码中主要做了如下改进：

1.增加多模板匹配机制

为了能精确地检测物体的旋转，引入多模板匹配。在代码中有4、8、16不同数量的模式可选。模板越多，对于旋转的识别越精确。 下图匹配模板数分别是1、4、8、16。

可以看到，单模版匹配已经无法正常识别跟踪了。模板数为4时，会有少量跟踪错误。当模板数为8和16时，跟踪的轨迹就相对精确了。 下图是采用8模板和单模板匹配的轨迹比较，可以看到，利用多模板匹配，可以较好识别旋转物体。 白色为单模版匹配轨迹，红色为多模板匹配轨迹。

同时考虑到卫星视频动目标一般运动形式是平移和旋转，没有缩放。所以经过优化的算法可以满足大部分需求。

2.增加轨迹平滑

通过对轨迹列表中最后两个点求均值作为最终的轨迹点，可以对提取的轨迹进行一定程度的平滑。

三、测试对比

下图是模拟飞机曲线飞行的视频。对其进行目标识别和轨迹提取后如下。

对应的飞行轨迹如下。

可以看到，相较于单模版匹配，能较好地提取运动目标和轨迹。而采用之前的单模版匹配算法，经过测试在刚转弯时就跟丢了，如下。