matlab物体跟踪与速度检测
时间: 2023-07-29 15:14:54 浏览: 47
好的,以下是一个基于光流法的物体跟踪和速度检测的Matlab代码:
```matlab
% 读取视频
v = VideoReader('input_video.mp4');
% 初始化
frame = readFrame(v);
h = size(frame, 1);
w = size(frame, 2);
prev_frame = rgb2gray(frame);
prev_features = detectSURFFeatures(prev_frame);
prev_points = prev_features.Location;
prev_valid_points = ones(size(prev_points, 1), 1);
bounding_box = [];
velocities = [];
% 逐帧处理
while hasFrame(v)
% 读取当前帧
frame = readFrame(v);
curr_frame = rgb2gray(frame);
% 计算光流
curr_points = detectSURFFeatures(curr_frame);
[curr_points, curr_valid_points] = estimateFlow(prev_frame, curr_frame, prev_points);
% 筛选目标
indices = find(curr_valid_points & prev_valid_points);
if ~isempty(indices)
curr_points = curr_points(indices, :);
prev_points = prev_points(indices, :);
velocities = [velocities; curr_points - prev_points];
bounding_box = [min(curr_points) max(curr_points) - min(curr_points)];
else
bounding_box = [];
end
% 显示结果
if ~isempty(bounding_box)
frame = insertShape(frame, 'Rectangle', bounding_box, 'LineWidth', 2);
end
imshow(frame);
% 更新状态
prev_frame = curr_frame;
prev_points = curr_points;
prev_valid_points = curr_valid_points;
end
% 计算速度
velocities = sqrt(sum(velocities.^2, 2));
velocities = velocities * v.FrameRate;
```
这个算法基于光流法,首先用SURF算法提取出前一帧和当前帧的特征点,然后用estimateFlow函数计算特征点的光流,根据光流筛选出目标并计算速度,最后在原始图像上用bounding box标识目标位置。这个算法只能跟踪单个目标,对于复杂场景效果不佳,但对于简单场景还是比较有效的。
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