Optical flow method moving target detection

Table of contents

Preface

动目标检测是计算机视觉领域的一个热门研究方向。传统的方法主要基于背景建模，但这些方法对于光照变化、遮挡和噪声敏感。因此，研究人员一直在寻找更加鲁棒和有效的技术来解决这一问题。光流法是一种基于运动信息的动目标检测方法，它通过分析相邻帧之间的像素位移来捕捉物体的运动。这种方法可以在不需要显式背景建模的情况下实现动目标检测。。

1. Effect display

2. Introduction to optical flow method

Optical flow refers to the vector field that describes the displacement of pixels in adjacent image frames. In the optical flow method, it is assumed that the pixel intensity between adjacent frames remains unchanged, that is, the movement of a pixel between two frames can be represented by a vector. This vector can represent the speed and direction of an object. The basic idea of the optical flow method is to find the corresponding points between adjacent frames by matching pixels in the image, and then calculate the displacement vector. These displacement vectors can be used to detect the motion and position of objects.

In the optical flow method, the optical flow equation is usually used to describe the pixel displacement. This equation is usually based on the assumption of constant brightness and spatial consistency, i.e. intensity changes between adjacent pixels are caused by the motion of the object. By solving these equations, the optical flow vector for each pixel can be calculated to achieve moving target detection.

3. Code display

The following is a simple optical flow moving target detection example implemented using Python and the OpenCV library:

import cv2
import numpy as np

def main():
    cap = cv2.VideoCapture(r"E:\movement_detection\move_mv.mp4")
    ret, frame1 = cap.read()
    frame1 = cv2.resize(frame1, None, fx=0.25, fy=0.25)
    prvs = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
    
    while True:
        ret, frame2 = cap.read()
        frame2 = cv2.resize(frame2, None, fx=0.25, fy=0.25)
        if not ret:
            break
        next = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)
    
        flow = cv2.calcOpticalFlowFarneback(prvs, next, None, 0.5, 3, 15, 3, 5, 1.2, 0)
    
        # 计算光流的幅值和方向
        magnitude, angle = cv2.cartToPolar(flow[..., 0], flow[..., 1])
    
        # 根据阈值创建二值遮罩，以便突出动态区域
        mask = cv2.inRange(magnitude, 2, 10)  # 根据实际情况调整阈值
    
        # 通过位运算将遮罩应用于当前帧
        result = cv2.bitwise_and(frame2, frame2, mask=mask)
    
        cv2.imshow("raw", frame2)
        cv2.imshow("Optical Flow-Based Object Detection", result)
    
        if cv2.waitKey(30) & 0xFF == 27:
            break
    
        prvs = next
    
    cap.release()
    cv2.destroyAllWindows()
if __name__ == '__main__':
    main()

This code uses the Farneback optical flow method to calculate the optical flow between adjacent frames and creates a binary mask based on the amplitude of the optical flow to highlight dynamic areas. You can adjust the threshold and other parameters to get the best results based on your video and specific needs.

Summarize

The optical flow method is a powerful moving target detection method that uses pixel displacement information to capture the movement of objects. It does not require explicit background modeling and is therefore robust to illumination changes and occlusions. However, the optical flow method also has its limitations, and it may not be suitable for fast motion and complex scenes. Therefore, in practical applications, it is necessary to choose an appropriate moving target detection method according to the specific situation. Optical flow method, as one of the methods, provides a powerful tool to solve the problem of moving target detection.
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