face recognition
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Preface
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 and 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 the head posture data to detect short-term changes in posture.
Correlation analysis : Analyze the correlation between different head posture parameters, for example, 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.
Spectrum 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>
// 计算均值
double calculateMean(const std::vector<double>& data) {
double sum = 0.0;
for (const double& value : data) {
sum += value;
}
return sum / data.size();
}
// 计算标准差
double calculateStandardDeviation(const std::vector<double>& data) {
double mean = calculateMean(data);
double variance = 0.0;
for (const double& value : data) {
variance += std::pow(value - mean, 2);
}
return std::sqrt(variance / data.size());
}
// 计算极差
double calculateRange(const std::vector<double>& data) {
auto minMax = std::minmax_element(data.begin(), data.end());
return *minMax.second - *minMax.first;
}
// 计算方差
double calculateVariance(const std::vector<double>& data) {
double mean = calculateMean(data);
double variance = 0.0;
for (const double& value : data) {
variance += std::pow(value - mean, 2);
}
return variance / data.size();
}
// 计算百分位数
double calculatePercentile(const std::vector<double>& 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];
}
// 滑动窗口统计
std::vector<double> calculateMovingAverage(const std::vector<double>& 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;
}
// 相关性分析
double calculateCorrelation(const std::vector<double>& x, const std::vector<double>& 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};
// 计算均值
double mean = calculateMean(data);
std::cout << "Mean: " << mean << std::endl;
// 计算标准差
double stdDeviation = calculateStandardDeviation(data);
std::cout << "Standard Deviation: " << stdDeviation << std::endl;
// 计算极差
double range = calculateRange(data);
std::cout << "Range: " << range << std::endl;
// 计算方差
double variance = calculateVariance(data);
std::cout << "Variance: " << variance << std::endl;
// 计算百分位数
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;
// 滑动窗口统计
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;
// 相关性分析
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;
}
Summarize
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.