Face recognition
Article directory
- face recognition
- Preface
- 1. Commonly used statistical methods
- 2. Specific implementation
- Summarize
Foreword
Statistical methods to assess the magnitude of driver head posture changes can help analyze driver attention and alertness.
1. Commonly used statistical methods
Statistical methods to assess the magnitude of driver head posture changes can help analyze driver attention and alertness. The following are some commonly used statistical methods:
Mean: Calculate the average of the head posture data, which can be used to understand the overall trend of the posture.
Standard Deviation: Calculate the standard deviation of the head posture data, which can measure the degree of dispersion of the posture data. A higher standard deviation indicates a larger change in head posture.
Range: Calculate the difference between the maximum value and the minimum value of the head posture data, which can reflect the range of posture.
Variance: The variance is the square of the standard deviation, which can provide directional information about the attitude data.
Percentiles: Calculate the percentiles of head posture data, such as the 25th and 75th percentiles, which can help identify outliers and posture distribution.
Sliding window statistics: Use sliding windows to calculate the moving average or moving standard deviation of head posture data to detect short-term changes in posture.
Correlation analysis: Analyze the correlation between different head posture parameters, such as the correlation between yaw, pitch and roll, to understand how they change together.
Time series analysis: Treating head posture data as a time series, time series analysis methods, such as autoregressive models (ARIMA) or Kalman filters, can be used to predict future posture changes.
Spectral analysis: Performing spectrum analysis on head posture data can identify frequency components and help analyze head posture changes at different frequencies.
Regression analysis: Use regression analysis to establish the relationship between head posture and other factors, such as the driver’s alertness or the driving environment, to understand the influence between them.
2. Specific implementation
#include <iostream>
#include <vector>
#include <cmath>
#include <algorithm>
// Calculate mean
double calculateMean(const std::vector<double> & amp; data) {<!-- -->
double sum = 0.0;
for (const double & amp; value : data) {<!-- -->
sum + = value;
}
return sum / data.size();
}
// Calculate standard deviation
double calculateStandardDeviation(const std::vector<double> & amp; data) {<!-- -->
double mean = calculateMean(data);
double variance = 0.0;
for (const double & amp; value : data) {<!-- -->
variance + = std::pow(value - mean, 2);
}
return std::sqrt(variance / data.size());
}
// Calculate range
double calculateRange(const std::vector<double> & amp; data) {<!-- -->
auto minMax = std::minmax_element(data.begin(), data.end());
return *minMax.second - *minMax.first;
}
// Calculate variance
double calculateVariance(const std::vector<double> & amp; data) {<!-- -->
double mean = calculateMean(data);
double variance = 0.0;
for (const double & amp; value : data) {<!-- -->
variance + = std::pow(value - mean, 2);
}
return variance / data.size();
}
// Calculate percentiles
double calculatePercentile(const std::vector<double> & amp; data, double percentile) {<!-- -->
std::vector<double> sortedData = data;
std::sort(sortedData.begin(), sortedData.end());
int index = static_cast<int>((percentile / 100.0) * (data.size() - 1));
return sortedData[index];
}
// Sliding window statistics
std::vector<double> calculateMovingAverage(const std::vector<double> & amp; data, int windowSize) {<!-- -->
std::vector<double> movingAverage;
for (int i = 0; i <= data.size() - windowSize; + + i) {<!-- -->
double sum = 0.0;
for (int j = i; j < i + windowSize; + + j) {<!-- -->
sum + = data[j];
}
movingAverage.push_back(sum / windowSize);
}
return movingAverage;
}
// Correlation analysis
double calculateCorrelation(const std::vector<double> & amp; x, const std::vector<double> & amp; y) {<!-- -->
if (x.size() != y.size()) {<!-- -->
throw std::invalid_argument("Input vectors must have the same size.");
}
double sumXY = 0.0;
double sumX = 0.0;
double sumY = 0.0;
double sumX2 = 0.0;
double sumY2 = 0.0;
for (size_t i = 0; i < x.size(); + + i) {<!-- -->
sumXY + = x[i] * y[i];
sumX + = x[i];
sumY + = y[i];
sumX2 + = x[i] * x[i];
sumY2 + = y[i] * y[i];
}
double numerator = x.size() * sumXY - sumX * sumY;
double denominator = std::sqrt((x.size() * sumX2 - sumX * sumX) * (x.size() * sumY2 - sumY * sumY));
return numerator / denominator;
}
int main() {<!-- -->
std::vector<double> data = {<!-- -->10.5, 12.2, 11.8, 9.7, 10.9, 11.5, 10.3, 12.8};
// Calculate mean
double mean = calculateMean(data);
std::cout << "Mean: " << mean << std::endl;
// Calculate standard deviation
double stdDeviation = calculateStandardDeviation(data);
std::cout << "Standard Deviation: " << stdDeviation << std::endl;
// Calculate range
double range = calculateRange(data);
std::cout << "Range: " << range << std::endl;
// Calculate variance
double variance = calculateVariance(data);
std::cout << "Variance: " << variance << std::endl;
// Calculate percentiles
double percentile25 = calculatePercentile(data, 25.0);
double percentile75 = calculatePercentile(data, 75.0);
std::cout << "25th Percentile: " << percentile25 << std::endl;
std::cout << "75th Percentile: " << percentile75 << std::endl;
// Sliding window statistics
int windowSize = 3;
std::vector<double> movingAvg = calculateMovingAverage(data, windowSize);
std::cout << "Moving Average: ";
for (double avg : movingAvg) {<!-- -->
std::cout << avg << " ";
}
std::cout << std::endl;
// Correlation analysis
std::vector<double> x = {<!-- -->1.0, 2.0, 3.0, 4.0, 5.0};
std::vector<double> y = {<!-- -->2.0, 3.0, 4.0, 5.0, 6.0};
double correlation = calculateCorrelation(x, y);
std::cout << "Correlation: " << correlation << std::endl;
return 0;
}
Summary
These statistical methods can be selected and combined according to specific application scenarios and data types to evaluate the magnitude of changes in the driver’s head posture and obtain information about the driver’s status and behavior.