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utils/tests/target_tracker/kalman_box_filter_test.py

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+"""kalman_box_filter_test.py —— KalmanBoxFilter 单元测试。
+
+移植自 reference-c++/target_tracker/tests/kalman_box_filter_test.cpp。
+覆盖（视觉追踪技术路线 §4）：初始化状态、静止目标收敛、匀速目标速度收敛
+且预测超前一个 dt、未初始化 predict 返回 None。
+"""
+
+import math
+
+import pytest
+
+from target_tracker.kalman_box_filter import BoxCXCYWH, KalmanBoxFilter
+
+
+def test_uninitialized_predict_returns_none():
+    kf = KalmanBoxFilter()
+    assert not kf.initialized
+    assert kf.predict() is None
+
+    # 未初始化时 update 无副作用
+    kf.update(BoxCXCYWH(100.0, 100.0, 20.0, 20.0))
+    assert not kf.initialized
+    assert kf.predict() is None
+    for v in kf.state:
+        assert v == 0.0
+
+
+def test_initiate_sets_state_correctly():
+    kf = KalmanBoxFilter()
+    kf.initiate(BoxCXCYWH(320.0, 240.0, 40.0, 30.0))
+    assert kf.initialized
+
+    expected = [320.0, 240.0, 40.0, 30.0, 0.0, 0.0, 0.0, 0.0]
+    state = kf.state
+    for i, want in enumerate(expected):
+        assert state[i] == want, f"index={i}"
+
+    # 初始化后 predict 返回状态前 4 维（速度为零 → 位置不变，§4.4）
+    pred = kf.predict()
+    assert pred is not None
+    assert pred.cx == pytest.approx(320.0, abs=1e-9)
+    assert pred.cy == pytest.approx(240.0, abs=1e-9)
+    assert pred.w == pytest.approx(40.0, abs=1e-9)
+    assert pred.h == pytest.approx(30.0, abs=1e-9)
+
+
+def test_static_target_converges_to_observation():
+    kf = KalmanBoxFilter()
+    z = BoxCXCYWH(100.0, 150.0, 40.0, 60.0)
+    kf.initiate(BoxCXCYWH(104.0, 156.0, 42.0, 62.0))  # 带初始偏差，验证收敛能力
+
+    for _ in range(50):
+        kf.predict()
+        kf.update(z)
+
+    state = kf.state
+    assert state[0] == pytest.approx(z.cx, abs=0.5)   # cx
+    assert state[1] == pytest.approx(z.cy, abs=0.5)   # cy
+    assert state[2] == pytest.approx(z.w, abs=0.5)    # w
+    assert state[3] == pytest.approx(z.h, abs=0.5)    # h
+    # 静止目标速度应收敛到约 0
+    assert state[4] == pytest.approx(0.0, abs=0.1)
+    assert state[5] == pytest.approx(0.0, abs=0.1)
+
+
+def test_constant_velocity_converges_and_predict_leads_one_dt():
+    kf = KalmanBoxFilter()
+    vx = 3.0   # 像素/帧
+    vy = 2.0   # 像素/帧
+
+    # 目标从 (100, 100) 出发匀速运动，每帧观测一次（predict → update）
+    z = BoxCXCYWH(100.0, 100.0, 30.0, 30.0)
+    kf.initiate(z)
+    for _ in range(60):
+        kf.predict()
+        z.cx += vx
+        z.cy += vy
+        kf.update(z)
+
+    state = kf.state
+    # 速度收敛到真实速度（像素/帧）
+    assert state[4] == pytest.approx(vx, abs=0.3), "vx 未收敛"
+    assert state[5] == pytest.approx(vy, abs=0.3), "vy 未收敛"
+    # 位置贴合当前观测
+    assert state[0] == pytest.approx(z.cx, abs=1.5)
+    assert state[1] == pytest.approx(z.cy, abs=1.5)
+
+    # 再预测一帧：应超前当前观测一个 dt（≈ 速度 × 1 帧）
+    pred = kf.predict()
+    assert pred is not None
+    assert pred.cx == pytest.approx(z.cx + vx, abs=1.0), "预测未超前一个 dt"
+    assert pred.cy == pytest.approx(z.cy + vy, abs=1.0), "预测未超前一个 dt"
+
+
+def test_small_target_noise_scaling_stable():
+    # 动态噪声不退化：小尺寸目标多帧观测仍稳定收敛（§4.2 噪声与尺寸成比例）
+    kf = KalmanBoxFilter()
+    z = BoxCXCYWH(50.0, 60.0, 4.0, 4.0)  # 小目标（4×4 像素）
+    kf.initiate(z)
+    for _ in range(30):
+        kf.predict()
+        kf.update(z)
+    state = kf.state
+    assert state[0] == pytest.approx(z.cx, abs=0.5)
+    assert state[1] == pytest.approx(z.cy, abs=0.5)
+    assert math.isfinite(state[2])
+    assert math.isfinite(state[3])

utils/tests/target_tracker/target_tracker_test.py

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+"""target_tracker_test.py —— TargetTracker 单元测试。
+
+移植自 reference-c++/target_tracker/tests/target_tracker_test.cpp。
+逐条覆盖 docs/视觉追踪技术路线.md §9.4 行为一致性验收用例（硬验收标准）：
+  1. 空检测帧序列：Coast 持续 max_lost_frames 帧，第 max_lost_frames+1 帧判丢
+  2. ID 严格递增不复用（含 Reset 后归零）
+  3. 锁定不被抢占；锁定目标死亡后切换到新目标
+  4. min_hits 回退：无已确认实测轨道时新轨道也可当选主目标
+  5. cost=1.0 恰好不构成匹配（严格小于）
+  6. 置信度优先：两轨道竞争同一检测，置信度高者获胜
+另含 NaN/Inf 防御与 §2.3/§6.3/§9.5 行为测试。
+"""
+
+import math
+
+import pytest
+
+from target_tracker.target_tracker import (Detection, FrameShape, TargetTracker,
+                                           TrackerConfig)
+
+
+def make_det(x1, y1, x2, y2, conf, cls=0):
+    return Detection(x1, y1, x2, y2, conf, cls)
+
+
+# ============================================================================
+# 验收用例 1（§9.4-1）：空检测帧序列
+# ============================================================================
+def test_empty_frames_coast_then_lost():
+    tracker = TargetTracker()  # 默认配置 max_lost_frames = 8
+    assert tracker.get_config().max_lost_frames == 8
+
+    # 第 1 帧：给出一个检测，建立轨道并锁定为主目标
+    r = tracker.update([make_det(100.0, 100.0, 140.0, 140.0, 0.9)])
+    assert r.tracked
+    assert r.primary_id is not None
+    assert not r.is_predicted      # 当帧有实测
+    assert r.lost_frames == 0
+    assert r.n_active == 1
+    assert r.box is not None
+    assert r.raw is not None       # 当帧关联到原始检测框
+
+    # 后续空检测帧：应持续 Coast 共 max_lost_frames 帧（is_predicted=True）
+    for i in range(1, 9):
+        r = tracker.update([])
+        assert r.tracked, f"第 {i} 帧 Coast 期间不应判丢"
+        assert r.is_predicted, f"第 {i} 帧应为纯预测输出"
+        assert r.lost_frames == i
+        assert r.primary_id is not None
+        assert r.box is not None      # Coast 期间继续输出预测框
+        assert r.raw is None          # Coast 时 raw 为空（§2.3）
+        assert r.confidence == pytest.approx(0.9)  # 继承最近实测置信度（§2.3）
+
+    # 第 max_lost_frames + 1 帧：轨道死亡回收 → tracked=False
+    r = tracker.update([])
+    assert not r.tracked
+    assert r.primary_id is None
+    assert r.box is None
+    assert r.lost_frames == 0  # 丢失时 lost_frames=0（§2.3）
+    assert r.n_active == 0
+
+
+# ============================================================================
+# 验收用例 2（§9.4-2）：ID 单调性
+# ============================================================================
+def test_id_strictly_increasing_no_reuse():
+    cfg = TrackerConfig()
+    cfg.max_lost_frames = 1  # 缩短 Coast 窗口，加速轨道死亡
+    tracker = TargetTracker(cfg)
+
+    # 目标 A 出现 → id 0
+    r = tracker.update([make_det(100.0, 100.0, 120.0, 120.0, 0.9)])
+    assert r.primary_id is not None
+    id_a = r.primary_id
+    assert id_a == 0
+
+    # 目标 A 消失至死亡（max_lost=1：第 2 帧 Coast、第 3 帧死亡回收）
+    tracker.update([])
+    r = tracker.update([])
+    assert not r.tracked
+    assert tracker.active_count() == 0
+
+    # 目标 B 出现（同一位置）：ID 不复用，严格递增 → id 1
+    r = tracker.update([make_det(100.0, 100.0, 120.0, 120.0, 0.9)])
+    assert r.primary_id is not None
+    id_b = r.primary_id
+    assert id_b == 1
+    assert id_b > id_a
+
+    # 目标 C 同帧新建 → id 2（同帧多目标亦严格递增）
+    r = tracker.update([make_det(100.0, 100.0, 120.0, 120.0, 0.9),
+                        make_det(400.0, 300.0, 430.0, 330.0, 0.8)])
+    assert tracker.active_count() == 2
+    max_id = max(tracker.active_tracks().keys())
+    assert max_id == 2
+
+    # Reset 后计数器归零（§6.2）：新轨道重新从 id 0 开始
+    tracker.reset()
+    assert tracker.active_count() == 0
+    assert not tracker.last_result().tracked
+    r = tracker.update([make_det(50.0, 50.0, 70.0, 70.0, 0.9)])
+    assert r.primary_id is not None
+    assert r.primary_id == 0
+
+
+# ============================================================================
+# 验收用例 3（§9.4-3）：锁定不被抢占
+# ============================================================================
+def test_lock_not_stolen_and_switch_after_death():
+    cfg = TrackerConfig()
+    cfg.max_lost_frames = 3
+    tracker = TargetTracker(cfg)
+
+    # 低置信度目标 A 建立并锁定
+    r = tracker.update([make_det(100.0, 100.0, 140.0, 140.0, 0.4)])
+    assert r.primary_id is not None
+    id_a = r.primary_id
+
+    # A 消失进入 Coast；期间出现更高置信度新目标 B（远离 A，不会被误关联）
+    r = tracker.update([make_det(500.0, 300.0, 540.0, 340.0, 0.95)])
+    assert r.primary_id is not None
+    assert r.primary_id == id_a, "Coast 期间锁定不应被高置信度目标抢占"
+    assert r.is_predicted
+    assert tracker.active_count() == 2  # A（Coast）与 B 并存
+
+    # 继续 Coast，锁定依旧延续
+    r = tracker.update([make_det(500.0, 300.0, 540.0, 340.0, 0.95)])
+    assert r.primary_id == id_a
+    assert r.is_predicted
+
+    # A 继续丢失直至死亡（max_lost=3：lost 达 4 帧时回收）
+    for _ in range(2):
+        r = tracker.update([make_det(500.0, 300.0, 540.0, 340.0, 0.95)])
+    # A 已死亡删除 → 锁定解除，重新选举切换到 B（此时 B 已确认且实测）
+    assert r.tracked
+    assert r.primary_id != id_a, "锁定目标死亡后应切换到新目标"
+    assert not r.is_predicted
+
+    # 后续 B 持续作为主目标
+    r = tracker.update([make_det(500.0, 300.0, 540.0, 340.0, 0.95)])
+    assert r.primary_id is not None
+    assert r.primary_id != id_a
+
+
+# ============================================================================
+# 验收用例 4（§9.4-4）：min_hits 回退
+# ============================================================================
+def test_min_hits_fallback_allows_new_track():
+    cfg = TrackerConfig()
+    cfg.min_hits = 5  # 抬高确认门槛，保证测试期间轨道始终未确认
+    tracker = TargetTracker(cfg)
+
+    # 首帧新建轨道 hits=1 < min_hits：候选池 confirmed 为空 → 退回 measured
+    r = tracker.update([make_det(200.0, 150.0, 240.0, 190.0, 0.6)])
+    assert r.tracked, "无已确认轨道时新轨道应能当选主目标"
+    assert r.primary_id is not None
+    assert r.primary_id == 0
+    assert not r.is_predicted
+
+    # 再跟几帧（hits 增长但仍未达 min_hits=5），依旧当选
+    for _ in range(3):
+        r = tracker.update([make_det(200.0, 150.0, 240.0, 190.0, 0.6)])
+        assert r.tracked
+        assert r.primary_id == 0
+
+
+# ============================================================================
+# 验收用例 5（§9.4-5）：cost 边界（cost = 1.0 恰好不匹配）
+# ============================================================================
+def test_cost_exactly_one_does_not_match():
+    cfg = TrackerConfig()
+    cfg.max_lost_frames = 1
+    cfg.max_association_dist = 200.0
+    cfg.dist_weight = 1.0  # 纯距离项，IoU 项权重为 0 且两框零交集
+    cfg.iou_weight = 0.0
+    tracker = TargetTracker(cfg)
+
+    # 建立轨道：中心 (100,100)
+    r = tracker.update([make_det(90.0, 90.0, 110.0, 110.0, 0.9)])
+    assert r.primary_id is not None
+    id_a = r.primary_id
+
+    # 下一帧检测中心 (100,300)：距离 = 200 = Dmax → cost = 1.0，恰好不匹配
+    r = tracker.update([make_det(90.0, 290.0, 110.0, 310.0, 0.9)])
+    # 未匹配 → 原轨道 Coast、检测新建轨道；两个目标并存
+    assert tracker.active_count() == 2, "cost=1.0 不应匹配，应新建轨道"
+    # 原轨道（锁定）进入 Coast：lost_frames=1
+    assert r.tracked
+    assert r.is_predicted
+    assert r.primary_id == id_a
+    assert r.lost_frames == 1
+
+    # 对照：距离略小于阈值（199px → cost=0.995 < 1.0）应匹配成功
+    tracker2 = TargetTracker(cfg)
+    r = tracker2.update([make_det(90.0, 90.0, 110.0, 110.0, 0.9)])
+    id_b = r.primary_id
+    r = tracker2.update([make_det(90.0, 289.0, 110.0, 309.0, 0.9)])
+    assert tracker2.active_count() == 1, "cost<1.0 应匹配成功，不应新建轨道"
+    assert r.primary_id is not None
+    assert r.primary_id == id_b
+    assert not r.is_predicted  # 实测命中
+
+
+# ============================================================================
+# 验收用例 6（§9.4-6）：置信度优先
+# ============================================================================
+def test_higher_confidence_track_wins_detection():
+    tracker = TargetTracker()
+
+    # 同帧建立两条轨道：A 置信度 0.5、B 置信度 0.9，相距 80px（不互相重叠）
+    r = tracker.update([make_det(100.0, 100.0, 120.0, 120.0, 0.5),
+                        make_det(180.0, 100.0, 200.0, 120.0, 0.9)])
+    assert tracker.active_count() == 2
+
+    # 找出两条轨道 ID（按置信度区分）
+    id_low = None
+    id_high = None
+    for track_id, track in tracker.active_tracks().items():
+        if track.confidence > 0.8:
+            id_high = track_id
+        else:
+            id_low = track_id
+    assert id_low is not None
+    assert id_high is not None
+
+    # 下一帧只给一个检测，放在两轨道预测中心之间（各距 40px，对称）：
+    # 距离项对两者相同，贪婪按置信度降序 → 高置信度轨道 B 优先匹配
+    r = tracker.update([make_det(140.0, 100.0, 160.0, 120.0, 0.7)])
+
+    tracks = tracker.active_tracks()
+    assert len(tracks) == 2
+    # 获胜者：命中（lost=0、hits=2、置信度更新）
+    assert tracks[id_high].lost_count == 0, "高置信度轨道应获胜"
+    assert tracks[id_high].hits == 2
+    assert tracks[id_high].confidence == pytest.approx(0.7)
+    # 失败者：未匹配进入 Coast（lost=1、hits 不变）
+    assert tracks[id_low].lost_count == 1, "低置信度轨道应落选"
+    assert tracks[id_low].hits == 1
+    assert tracks[id_low].confidence == pytest.approx(0.5)
+
+
+# ============================================================================
+# NaN/Inf 防御（评审修复项）
+# ============================================================================
+def test_non_finite_detections_are_discarded():
+    tracker = TargetTracker()
+    nan = math.nan
+    inf = math.inf
+
+    # 第 1 帧：建立正常轨道
+    r = tracker.update([make_det(100.0, 100.0, 140.0, 140.0, 0.9)])
+    assert r.tracked
+
+    # 第 2 帧：混入 NaN 坐标 / Inf 坐标 / NaN 置信度检测与一个正常检测：
+    # 非有限检测全部被丢弃，仅正常检测参与关联，不新建轨道
+    r = tracker.update([make_det(nan, 100.0, 140.0, 140.0, 0.9),
+                        make_det(100.0, -inf, 140.0, 140.0, 0.9),
+                        make_det(100.0, 100.0, nan, 140.0, 0.9),
+                        make_det(102.0, 102.0, 142.0, 142.0, nan),
+                        make_det(101.0, 101.0, 141.0, 141.0, 0.9)])
+    assert tracker.active_count() == 1, "非有限检测不应新建轨道"
+    assert r.tracked
+    assert not r.is_predicted  # 正常检测命中，实测输出
+    # 主目标输出未被 NaN 污染
+    assert math.isfinite(r.center[0])
+    assert math.isfinite(r.center[1])
+    assert r.box is not None
+    assert math.isfinite(r.box.x1) and math.isfinite(r.box.y1)
+    assert math.isfinite(r.box.x2) and math.isfinite(r.box.y2)
+    assert math.isfinite(r.confidence)
+    assert r.raw is not None  # 实测帧 raw 有值
+
+    # 第 3 帧：仅含 NaN 检测 → 等价空帧，轨道 Coast 不崩溃
+    r = tracker.update([make_det(nan, nan, nan, nan, nan)])
+    assert r.tracked
+    assert r.is_predicted
+    assert r.lost_frames == 1
+    assert r.raw is None  # Coast 时 raw 为空（§2.3）
+    assert math.isfinite(r.center[0])
+
+    # 持续 NaN 检测直至轨道死亡回收，全程不崩溃
+    for _ in range(10):
+        r = tracker.update([make_det(nan, 0.0, 10.0, 10.0, 0.9)])
+    assert not r.tracked
+    assert tracker.active_count() == 0
+
+
+# ============================================================================
+# 补充行为测试（§2.3 / §6.3 / §9.5）
+# ============================================================================
+
+def test_lost_result_fields_are_empty():
+    tracker = TargetTracker()
+    r = tracker.update([])  # 空帧序列起点：无任何轨道
+    assert not r.tracked
+    assert r.primary_id is None
+    assert r.box is None
+    assert r.raw is None
+    assert r.lost_frames == 0
+    assert r.n_active == 0
+    assert r.confidence == 0.0
+
+    # LastResult 返回同一快照（§9.5 跨线程读取入口）
+    cached = tracker.last_result()
+    assert not cached.tracked
+
+
+def test_coasting_fallback_chooses_nearest_to_frame_center():
+    # Coast 兜底选举：无实测轨道时选离画面中心最近的 Coast 轨道（§6.3）。
+    # 锁定延续优先：需先让锁定轨道死亡解除锁定，才会进入重新选举分支。
+    cfg = TrackerConfig()
+    cfg.max_lost_frames = 2
+    tracker = TargetTracker(cfg)
+
+    # F1：角落目标（高置信度）先建立并被锁定
+    tracker.update([make_det(10.0, 10.0, 30.0, 30.0, 0.95)])
+    # F2：画面中心附近目标建立；角落目标丢失(1)
+    tracker.update([make_det(300.0, 220.0, 340.0, 260.0, 0.9)])
+    # F3：远处目标建立；角落丢失(2)、中心目标丢失(1)
+    tracker.update([make_det(600.0, 450.0, 620.0, 470.0, 0.85)])
+    # F4：全部丢失：角落丢失(3) > max_lost 死亡解除锁定；
+    # 其余两条 Coast 存活 → 重新选举选离画面中心最近者
+    r = tracker.update([], FrameShape(480, 640))
+    assert r.tracked
+    assert r.is_predicted
+    assert r.center[0] == pytest.approx(320.0, abs=25.0)   # 靠近画面中心 (320,240)
+    assert r.center[1] == pytest.approx(240.0, abs=25.0)
+    assert r.n_active == 2                                 # 角落轨道已回收
+
+
+def test_locked_primary_kept_while_measured():
+    # 锁定延续优先于置信度选举（§6.3 ①）
+    tracker = TargetTracker()
+    r = tracker.update([make_det(100.0, 100.0, 120.0, 120.0, 0.4)])
+    assert r.primary_id is not None
+    locked_id = r.primary_id
+
+    # 高置信度新目标出现并持续，锁定目标也持续实测 → primary_id 不变
+    for i in range(5):
+        r = tracker.update([make_det(100.0 + i, 100.0, 120.0 + i, 120.0, 0.4),
+                            make_det(400.0, 300.0, 430.0, 330.0, 0.95)])
+        assert r.primary_id is not None
+        assert r.primary_id == locked_id
+        assert not r.is_predicted
+
+
+def test_default_frame_center_is_320x240():
+    # frame_shape 缺省按 640×480 画面中心 (320,240) 计算（§8.2 / §2.2）
+    cfg = TrackerConfig()
+    cfg.max_lost_frames = 2
+    tracker = TargetTracker(cfg)
+
+    # F1：角落目标先建立并被锁定（高置信度）
+    tracker.update([make_det(0.0, 0.0, 20.0, 20.0, 0.95)])
+    # F2：左侧目标（中心 (280,240)，距默认中心 40px）建立
+    tracker.update([make_det(270.0, 230.0, 290.0, 250.0, 0.9)])
+    # F3：右侧目标（中心 (500,240)，距默认中心 180px）建立
+    tracker.update([make_det(490.0, 230.0, 510.0, 250.0, 0.85)])
+    # F4：全部丢失：锁定轨道死亡解除锁定，两条 Coast 轨道兜底选举
+    r = tracker.update([])
+    assert r.tracked
+    assert r.is_predicted
+    assert r.center[0] == pytest.approx(280.0, abs=30.0)   # 左侧更近，当选
+    assert r.center[1] == pytest.approx(240.0, abs=30.0)

utils/tests/target_tracker/tracklet_test.py

@@ -0,0 +1,159 @@
+"""tracklet_test.py —— Tracklet 与几何工具单元测试。
+
+移植自 reference-c++/target_tracker/tests/tracklet_test.cpp。
+覆盖（视觉追踪技术路线 §3.2/§5.2/§6.1/§7）：框互转、IoU 边界、
+IsDead/IsCoasting 判定（严格大于）、Predict/Update/MarkMiss 计数语义。
+"""
+
+import pytest
+
+from target_tracker.tracklet import (BoxXYXY, BoxCXCYWH, Tracklet, compute_iou,
+                                     to_center_size, to_xyxy)
+
+
+# ---- 框表示互转（§7） ----
+
+def test_round_trip_preserves_values():
+    box = BoxXYXY(10.0, 20.0, 50.0, 80.0)
+    c = to_center_size(box)
+    assert c.cx == 30.0   # (10+50)/2
+    assert c.cy == 50.0   # (20+80)/2
+    assert c.w == 40.0    # 50−10
+    assert c.h == 60.0    # 80−20
+
+    back = to_xyxy(c)
+    assert back.x1 == box.x1
+    assert back.y1 == box.y1
+    assert back.x2 == box.x2
+    assert back.y2 == box.y2
+
+
+# ---- IoU 边界（§5.2） ----
+
+def test_identical_boxes_return_one():
+    a = BoxXYXY(10.0, 10.0, 30.0, 30.0)
+    assert compute_iou(a, a) == pytest.approx(1.0, abs=1e-12)  # 完全重合 = 1
+
+
+def test_disjoint_boxes_return_zero():
+    a = BoxXYXY(0.0, 0.0, 10.0, 10.0)
+    b = BoxXYXY(20.0, 20.0, 30.0, 30.0)
+    assert compute_iou(a, b) == 0.0  # 零交集
+
+
+def test_touching_edges_return_zero():
+    # 仅边/角相接：交集宽高为 0 → 交集面积 ≤ 0 返回 0（§5.2）
+    a = BoxXYXY(0.0, 0.0, 10.0, 10.0)
+    b = BoxXYXY(10.0, 0.0, 20.0, 10.0)   # 右边缘相接
+    assert compute_iou(a, b) == 0.0
+    c = BoxXYXY(10.0, 10.0, 20.0, 20.0)  # 角点相接
+    assert compute_iou(a, c) == 0.0
+
+
+def test_empty_box_returns_zero():
+    normal = BoxXYXY(0.0, 0.0, 10.0, 10.0)
+    zero_area = BoxXYXY(5.0, 5.0, 5.0, 5.0)    # 零面积
+    inverted = BoxXYXY(10.0, 10.0, 0.0, 0.0)   # 反向（负宽高）
+    assert compute_iou(normal, zero_area) == 0.0
+    assert compute_iou(zero_area, normal) == 0.0
+    assert compute_iou(normal, inverted) == 0.0
+    assert compute_iou(zero_area, zero_area) == 0.0
+
+
+def test_partial_overlap_is_correct():
+    # A = [0,0,10,10]（面积 100），B = [5,5,15,15]（面积 100），交集 25
+    a = BoxXYXY(0.0, 0.0, 10.0, 10.0)
+    b = BoxXYXY(5.0, 5.0, 15.0, 15.0)
+    assert compute_iou(a, b) == pytest.approx(25.0 / 175.0, abs=1e-12)
+    assert compute_iou(b, a) == pytest.approx(25.0 / 175.0, abs=1e-12)  # 对称性
+
+
+# ---- 构造与派生状态（§3.2 / §6.1） ----
+
+def test_construction_initial_fields():
+    t = Tracklet(7, BoxCXCYWH(100.0, 100.0, 20.0, 20.0), 0.9, 1, 8)
+
+    assert t.track_id == 7
+    assert t.lost_count == 0
+    assert t.max_lost == 8
+    assert t.confidence == 0.9
+    assert t.cls_id == 1
+    assert t.age == 1   # 创建时为 1（§3.2）
+    assert t.hits == 1  # 创建时为 1（§3.2）
+    assert t.last_center == (100.0, 100.0)
+    assert t.last_box is not None  # 创建当帧即赋 last_box（§7 步骤 5）
+    assert t.last_box.x1 == 90.0
+    assert t.last_box.y1 == 90.0
+    assert t.last_box.x2 == 110.0
+    assert t.last_box.y2 == 110.0
+    assert not t.is_dead
+    assert not t.is_coasting
+
+
+def test_is_dead_uses_strictly_greater():
+    # IsDead 使用严格大于：lost_count == max_lost 仍存活（§6.1 注记 2）
+    t = Tracklet(0, BoxCXCYWH(100.0, 100.0, 20.0, 20.0), 0.9, 0, 8)
+    for _ in range(8):
+        t.mark_miss()
+        assert not t.is_dead, f"lost_count={t.lost_count} 未超过 max_lost 不应判死"
+    assert t.lost_count == 8
+    t.mark_miss()  # lost_count = 9 > 8
+    assert t.is_dead
+
+
+def test_is_coasting_when_lost_positive():
+    t = Tracklet(0, BoxCXCYWH(100.0, 100.0, 20.0, 20.0), 0.9, 0, 8)
+    assert not t.is_coasting
+    t.mark_miss()
+    assert t.is_coasting  # lost_count > 0 即 Coast（§3.2）
+
+
+# ---- 生命周期方法计数语义（§7） ----
+
+def test_predict_advances_age_and_updates_last_state():
+    t = Tracklet(0, BoxCXCYWH(100.0, 100.0, 20.0, 20.0), 0.9, 0, 8)
+    age_before = t.age
+    t.predict()  # 速度为零：位置不变，但 age+1、last_box 更新为预测框
+    assert t.age == age_before + 1
+    assert t.last_box is not None
+    assert t.last_center[0] == pytest.approx(100.0, abs=1e-9)
+    assert t.last_center[1] == pytest.approx(100.0, abs=1e-9)
+    # hits / lost_count / confidence 不受预测影响
+    assert t.hits == 1
+    assert t.lost_count == 0
+    assert t.confidence == 0.9
+
+
+def test_update_resets_lost_and_increments_hits():
+    t = Tracklet(0, BoxCXCYWH(100.0, 100.0, 20.0, 20.0), 0.9, 0, 8)
+    t.mark_miss()
+    t.mark_miss()
+    assert t.lost_count == 2
+    assert t.is_coasting
+
+    age_before = t.age
+    box = BoxXYXY(150.0, 160.0, 170.0, 180.0)
+    t.update(box, 0.75, 2)
+
+    assert t.lost_count == 0   # 匹配成功清零（§7）
+    assert t.hits == 2         # hits+1
+    assert t.age == age_before + 1  # age+1
+    assert t.confidence == 0.75
+    assert t.cls_id == 2
+    assert t.last_center == (160.0, 170.0)  # ((150+170)/2, (160+180)/2)
+    assert t.last_box is not None
+    assert t.last_box.x1 == box.x1
+    assert t.last_raw_box is not None  # 实测框同步记录
+    assert t.last_raw_box.x2 == box.x2
+    assert not t.is_coasting
+
+
+def test_mark_miss_increments_lost_and_age():
+    t = Tracklet(0, BoxCXCYWH(100.0, 100.0, 20.0, 20.0), 0.9, 0, 8)
+    age_before = t.age
+    t.mark_miss()
+    assert t.lost_count == 1
+    assert t.age == age_before + 1
+    # MarkMiss 不影响 hits / confidence
+    assert t.hits == 1
+    assert t.confidence == 0.9