introduction
Human movement is a very important source of information that conveys people's intentions, emotions, and behaviors. Therefore, it is a challenging task for computers to accurately recognize and understand human movements. Human Action Recognition in the field of computer vision aims to automatically identify and interpret human movement patterns and behaviors from images or videos. This article will introduce the importance, application fields and common computer vision algorithms of human action recognition.
Importance and application areas
Human action recognition has important application value in many fields. Here are some common application areas:
Video surveillance and security
Human motion recognition can help the monitoring system automatically detect and alarm abnormal behaviors, such as theft, violence, etc. It can be used for security in public places, banks, airports and other places.
Human-computer interaction and virtual reality
Human action recognition can be used in human-computer interaction systems, such as gesture recognition and posture control. It can help users interact with computers intuitively and provide a more natural and convenient operation method. In addition, in the field of virtual reality, human action recognition can be used to track the user's movements in real time to achieve a more realistic interactive experience.
Movement Analysis and Rehabilitation Assistance
Human motion recognition can be used for motion analysis and rehabilitation assistance. It can help athletes improve their technique and improve their sports performance. At the same time, it can also be used as rehabilitation assistance to help rehabilitation patients monitor and evaluate the effects of rehabilitation training.
media & entertainment
Human motion recognition can be used for movie special effects, games and virtual character control. It allows virtual characters to react in real time according to the user's actions, enhancing the entertainment experience.
Computer Vision Algorithms
Human action recognition is a complex and diverse task that requires a combination of multiple computer vision algorithms to achieve. Here are some common algorithms:
Deep learning based methods
Deep learning has made significant breakthroughs in human action recognition. Methods based on convolutional neural networks (CNN) can extract features from images or videos and use recurrent neural networks (RNN) or long short-term memory networks (LSTM) to capture time series information. These methods have good recognition performance, but require a large amount of annotated data and computing resources.
Methods based on attitude estimation
Pose estimation is an important preprocessing step for human action recognition. It can extract human body posture information through joint point detection and tracking. Methods based on attitude estimation can use information such as joint trajectories and joint angles to represent and identify human movements.
Optical flow based methods
Optical flow is a method of representing the movement of pixels in an image. Optical flow-based methods can capture the motion information of the human body by calculating the optical flow field between consecutive frames. These methods are usually used to process video sequences and are robust to rapid changes in action and changes in details.
The following is an algorithm for optical flow-based methods to implement optical flow tracking:
- First, two consecutive images are selected as input, called the previous frame and the current frame respectively.
- Preprocess the previous frame and current frame, such as grayscale or color denoising.
- Use an optical flow algorithm (such as the Farneback algorithm or the Lucas-Kanade algorithm) to calculate the optical flow field between the previous frame and the current frame. The optical flow field represents the motion information of each pixel in the image.
- Depending on the calculated optical flow field, different methods can be selected to track the optical flow.
- A common method is to use the direction and magnitude of the optical flow vector to estimate the trajectory of the target. You can set a threshold to filter out optical flow vectors with a certain amount of motion, and then estimate the target's motion trajectory based on the position information of these optical flow vectors.
- Another method is to use the direction and magnitude of the optical flow vector to estimate the moving speed of the target. The average or maximum value of the optical flow vector can be calculated to obtain the average or maximum movement speed of the target.
- Further analysis and applications can be performed based on the tracking results, such as target detection, target tracking, action recognition, etc. It should be noted that the selection and parameter settings of the optical flow algorithm will have an impact on the tracking results, so in practical applications it needs to be adjusted and optimized according to specific conditions. The following is a sample code for optical flow tracking based on the Farneback algorithm:
pythonCopy codeimport cv2
import numpy as np
# 读取视频文件
cap = cv2.VideoCapture('input.mp4')
# 读取第一帧
ret, frame1 = cap.read()
prvs = cv2.cvtColor(frame1,cv2.COLOR_BGR2GRAY)
# 创建光流图像的掩码
hsv = np.zeros_like(frame1)
hsv[...,1] = 255
while(1):
# 读取当前帧
ret, frame2 = cap.read()
next = cv2.cvtColor(frame2,cv2.COLOR_BGR2GRAY)
# 计算光流
flow = cv2.calcOpticalFlowFarneback(prvs,next, None, 0.5, 3, 15, 3, 5, 1.2, 0)
# 将光流转换为颜色图像
mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])
hsv[...,0] = ang*180/np.pi/2
hsv[...,2] = cv2.normalize(mag,None,0,255,cv2.NORM_MINMAX)
rgb = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR)
# 显示光流图像
cv2.imshow('Optical Flow',rgb)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 更新前一帧
prvs = next
cap.release()
cv2.destroyAllWindows()In this example code, we use the calcOpticalFlowFarneback function from the OpenCV library to calculate the optical flow between two frames. We read the video file and use the first frame as the previous frame. We then use a while loop to read each frame of the video and calculate the optical flow between each frame and the previous frame. Next, we convert the optical flow into a color image and display it in the window. Finally, we stop optical flow tracking and close the window by pressing the 'q' key.
Methods based on feature extraction and classification
Traditional computer vision methods typically use hand-designed features to represent human actions and use machine learning algorithms for classification. For example, features such as optical flow histograms and shape descriptors are used for action recognition. These methods have good performance on some small-scale data sets.
The following is a sample code for implementing a pose estimation based approach:
pythonCopy codeimport cv2
import numpy as np
# 加载预训练的人脸检测器和姿态估计器
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
pose_estimator = cv2.dnn.readNetFromTensorflow('pose_deploy_linevec_faster_4_stages.pb')
# 读取输入的图像
image = cv2.imread('input.jpg')
# 将图像转换为灰度图像
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 人脸检测
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
# 遍历检测到的人脸
for (x, y, w, h) in faces:
# 提取人脸区域
face_roi = image[y:y+h, x:x+w]
# 对人脸区域进行姿态估计
blob = cv2.dnn.blobFromImage(face_roi, 1.0, (224, 224), (104.0, 177.0, 123.0), False, False)
pose_estimator.setInput(blob)
output = pose_estimator.forward()
# 解析姿态估计结果
for i in range(0, output.shape[2]):
confidence = output[0, 0, i, 2]
if confidence > 0.5:
# 获取关键点坐标
x_coord = int(output[0, 0, i, 3] * w)
y_coord = int(output[0, 0, i, 4] * h)
# 在人脸区域绘制关键点
cv2.circle(face_roi, (x_coord, y_coord), 3, (0, 255, 0), -1)
# 在原图像中绘制人脸和姿态估计结果
cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)
cv2.imshow('Output', image)
# 显示结果
cv2.waitKey(0)
cv2.destroyAllWindows()This code implements a pose estimation based method using the OpenCV library. First, the pretrained face detector and pose estimator are loaded. Then, the input image is read and converted into a grayscale image. Next, use a face detector to detect face areas in the image. For each detected face, the face region is extracted and its pose is estimated. By analyzing the pose estimation results, the key point coordinates of the face can be obtained. Finally, the drawn key points and face frames are added to the original image, and the results are displayed.
Challenges and prospects
Although human action recognition has widespread applications in many fields, there are still many challenges that need to be overcome. Here are some challenges:
- Viewing angle changes and occlusion: Human action recognition requires accurate recognition of human actions under different viewing angles and occlusions.
- Multi-person action recognition: How to distinguish and identify different actions between multiple people is a challenging problem.
- Lack of data and difficulty in labeling: Obtaining large-scale labeled data sets is a key issue for human action recognition.
- Real-time and efficiency: Real-time is an important requirement in some application fields, and efficient algorithms and systems are required to meet this requirement. In the future, with the continuous development of deep learning and computer vision technology, human action recognition will achieve greater breakthroughs. At the same time, combined with information from other sensors (such as depth sensors, inertial sensors, etc.), the movement of the human body will be more accurately analyzed and understood.
in conclusion
Human action recognition is an important research direction in the field of computer vision and has wide application value. By using algorithms such as deep learning, pose estimation, optical flow, and feature extraction, automatic recognition and interpretation of human actions can be achieved. However, there are still many challenges that need to be solved, such as perspective changes, multi-person action recognition, and lack of data. In the future, with the development of technology, we can look forward to more accurate and efficient human action recognition algorithms to provide people with better services and experiences.