目标跟踪质量计算方法

目标跟踪质量是评价视频目标跟踪效果的重要指标，主要通过计算跟踪器输出的轨迹和真实轨迹之间的相似度来确定。下面给出一种常见的目标跟踪质量计算方法，具体实现使用Python语言和OpenCV库：

import numpy as np
import cv2

def calculate_IOU(bbox1, bbox2):
    """
    计算两个矩形框的交并比
    bbox: (x1, y1, w, h)
    """
    x1, y1, w1, h1 = bbox1
    x2, y2, w2, h2 = bbox2
    area1, area2 = w1 * h1, w2 * h2
    xA = max(x1, x2)
    yA = max(y1, y2)
    xB = min(x1 + w1, x2 + w2)
    yB = min(y1 + h1, y2 + h2)
    interArea = max(0, xB - xA) * max(0, yB - yA)
    iou = interArea / float(area1 + area2 - interArea)
    return iou

def calculate_quality(tracker, ground_truth):
    """
    计算目标跟踪质量，返回IOU均值和成功率
    tracker: list of bounding boxes [(x1, y1, w, h), (x1, y1, w, h), ...]
    ground_truth: list of bounding boxes [(x1, y1, w, h), (x1, y1, w, h), ...]
    """
    if len(tracker) != len(ground_truth):
        raise ValueError("跟踪器输出的轨迹数量与真实轨迹数量不一致！")
    
    IOUs = []
    success_count = 0
    
    for i in range(len(tracker)):