【Practical Exercise】MATLAB Human Behavior Detection and Recognition

# 2.1 Human Activity Detection Algorithms

Human activity detection algorithms form the foundation of human activity analysis and recognition. Their purpose is to extract feature info***monly used human activity detection algorithms include background modeling and foreground segmentation, motion target detection, and so on.

### 2.1.1 Background Modeling and Foreground Segmentation

Background modeling and foreground segmentation algorithms aim to differentiate between the background and foreground (i.e., humans) within video frames. Background modeling typically uses statistical models such as Gaussian Mixture Models (GMM) or Kernel Density Estimation (KDE) to model the background pixels. When a new frame arrives, each pixel is compared against the background model, and if the pixel differs significantly from the model, it is marked as a foreground pixel.

### 2.1.2 Motion Target Detection

Motion ta***monly used motion target detection algorithms include frame differencing, optical flow, and background subtraction. Frame differencing detects motion targets by calculating the pixel differences between adjacent frames. Optical flow detects motion targets by computing the movement trajectories of pixels across consecutive frames. Background subtraction detects motion targets by comparing the current frame with a background model.

# 2. MATLAB Implementation of Human Activity Detection and Recognition Algorithms

### 2.1 Human Activity Detection Algorithms

#### 2.1.1 Background Modeling and Foreground Segmentation

Background modeling aims to establish a statistical model of the background in a video sequence in order to separate the foreground objects (such as humans) ***mon background modeling methods include:

- **Gaussian Mixture Model (GMM)**: Assumes that the background value of each pixel follows a Gaussian distribution, and represents the background by maintaining a mixture model of multiple Gaussians.
- **Average Background Method**: Calculates the average of all frames in the video sequence as the background model.
- **Median Filtering Method**: Uses a median filter to filter each pixel in the video sequence, eliminating the influence of foreground objects.

Foreground segmentation i***mon foreground segmentation methods include:

- **Frame Differencing**: Calculates the difference between the current frame and the background model, and pixels that exceed a threshold are considered foreground.
- **Optical Flow Method**: Utilizes optical flow information to detect moving pixels and marks them as foreground.
- **Morphological Operations**: Uses morphological operations such as dilation and erosion to remove noise and fill in foreground regions.

#### 2.1***

***mon motion target detection methods include:

- **Frame Differencing**: Calculates the difference between adjacent frames and uses a threshold to detect moving pixels.
- **Background Subtraction Method**: Subtracts the current frame from the background model and uses a threshold to detect moving pixels.
- **Optical Flow Method**: Utilizes optical flow information to detect moving pixels.

### 2.2 Human Activity Recognition Algorithms

#### 2.2.1 Feature Extraction

Human activity reco***mon feature extraction methods include:

- **Space-Time Interest Points (STIP)**: Detects interest points with spatial and temporal changes in video sequences and extracts their local feature descriptors.
- **Histogram of Optical Flow (HOF)**: Calculates the histogram of optical flow in video sequences to capture motion information.
- **Trajectory Descriptors**: Extracts features from the trajectories of foreground objects, such as length, speed, and acceleration.

#### 2.2.2 Classifier Training

Once features have been extracted, ***mon classifiers include:

- **Support Vector Machine (SVM)**: A binary classifier that finds the optimal hyperplane in feature space to classify data points.
- **Random Forest**: An ensemble classifier that improves accuracy by combining multiple decision trees.
- **Deep Learning Models**: Such as Convolutional Neural Networks (CNN), which can learn features from data automatically and perform classification.

#### 2.2.3 Activity Recognition

The trained classifier is used to recognize activities in video sequences. The process typically involves the following steps:

- **Feature Extraction**: Extracts features from the video sequence.
- **Classification**: Classifies the extracted features using the tra