迁移 Flutter 端与训练脚本，模型/训练产物移出 git（遵循纯代码约定）

flutter_app/lib/detection/background_model.dart

@@ -0,0 +1,94 @@
+import 'dart:math' as math;
+import 'dart:typed_data';
+
+import 'detection_result.dart';
+import 'motion_aggregator.dart';
+
+/// 静态场景背景建模：运行均值 + 方差，帧差高于自适应阈值的像素记为"新出现"，
+/// 分块聚合为新颖区域（novelty）。
+///
+/// 固定机位下，常驻物体（键盘/石头/文字）永远属于背景、不产生新颖区域；
+/// 走进画面的目标（野鸡移动/新出现）才会触发。比相邻帧差分更强的证据：
+/// 风吹草动是持续的背景更新，不会长期标记为新颖。
+class BackgroundModel {
+  final int maxWidth;
+  final int maxHeight;
+
+  static const double learnRate = 0.05;
+  static const double kSigma = 2.5;
+  static const int minDiff = 15;
+  static const int minPixels = 12;
+
+  Float32List? _mean;
+  Float32List? _var;
+  int _tw = 0;
+
+  BackgroundModel({this.maxWidth = 128, this.maxHeight = 128});
+
+  /// 后台 isolate 用原始数据接口（与 MotionDetector 同源：直接取 planes[0]）。
+  List<MotionRegion> updateRaw(
+      Uint8List yPlane, int yStride, int width, int height) {
+    final scale =
+        maxWidth / width < maxHeight / height ? maxWidth / width : maxHeight / height;
+    final tw = (width * scale).toInt().clamp(1, maxWidth);
+    final th = (height * scale).toInt().clamp(1, maxHeight);
+    if (tw == 0 || th == 0) return const [];
+
+    final gray = Float32List(tw * th);
+    for (var oy = 0; oy < th; oy++) {
+      final sy = (oy / scale).toInt().clamp(0, height - 1);
+      final idx = oy * tw;
+      for (var ox = 0; ox < tw; ox++) {
+        final sx = (ox / scale).toInt().clamp(0, width - 1);
+        gray[idx + ox] = yPlane[sy * yStride + sx].toDouble();
+      }
+    }
+    return update(gray, tw, th);
+  }
+
+  List<MotionRegion> update(Float32List gray, int tw, int th) {
+    final n = gray.length;
+    final mean = _mean;
+    final variance = _var;
+    if (mean == null || variance == null || mean.length != n || _tw != tw) {
+      _mean = Float32List.fromList(gray);
+      _var = Float32List(n);
+      _tw = tw;
+      return const [];
+    }
+
+    final diff = Uint8List(n);
+    var fgCount = 0;
+    for (var i = 0; i < n; i++) {
+      final g = gray[i];
+      final m = mean[i];
+      final d = (g - m).abs();
+      if (d > kSigma * math.sqrt(variance[i]) + minDiff) {
+        diff[i] = 1;
+        fgCount++;
+        // 前景像素不更新背景，避免把移动目标吸收进背景
+      } else {
+        // 静态像素缓慢吸收进背景，适应光照漂移
+        final nm = m + learnRate * (g - m);
+        mean[i] = nm;
+        variance[i] =
+            variance[i] + learnRate * ((g - nm) * (g - nm) - variance[i]);
+      }
+    }
+
+    // 全屏大变化 → 相机移动/场景切换，重建背景
+    if (fgCount > n ~/ 2) {
+      _mean = null;
+      _var = null;
+      return const [];
+    }
+    if (fgCount < minPixels) return const [];
+    return MotionAggregator.aggregate(diff, tw, th);
+  }
+
+  /// 相机切换后重置，避免旧场景背景
+  void reset() {
+    _mean = null;
+    _var = null;
+  }
+}