一、技术选型与开发环境准备

1.1 开发工具链配置

Android Studio作为官方IDE，需配置NDK（Native Development Kit）以支持C++代码编译。建议使用最新稳定版（如Electric Eel），在SDK Manager中安装：

CMake 3.22+
LLDB调试器
NDK r25+

项目级build.gradle需添加：

android {
    defaultConfig {
        externalNativeBuild {
            cmake {
                cppFlags "-std=c++17"
                arguments "-DANDROID_STL=c++_shared"
            }
        }
    }
    externalNativeBuild {
        cmake {
            path "src/main/cpp/CMakeLists.txt"
        }
    }
}

1.2 计算机视觉库集成

OpenCV Android SDK集成步骤：

下载OpenCV Android包（4.5.5+版本）
将sdk/native/libs目录下对应ABI的.so文件拷贝至app/src/main/jniLibs

在AndroidManifest.xml添加相机权限：

<uses-permission android:name="android.permission.CAMERA" />
<uses-feature android:name="android.hardware.camera" />
<uses-feature android:name="android.hardware.camera.autofocus" />

二、核心检测算法实现

2.1 帧差法实现

public Bitmap frameDifference(Bitmap prevFrame, Bitmap currFrame) {
    int width = prevFrame.getWidth();
    int height = prevFrame.getHeight();
    int[] prevPixels = new int[width * height];
    int[] currPixels = new int[width * height];
    prevFrame.getPixels(prevPixels, 0, width, 0, 0, width, height);
    currFrame.getPixels(currPixels, 0, width, 0, 0, width, height);
    int[] resultPixels = new int[width * height];
    for (int i = 0; i < width * height; i++) {
        int prevR = (prevPixels[i] >> 16) & 0xFF;
        int currR = (currPixels[i] >> 16) & 0xFF;
        int diff = Math.abs(currR - prevR);
        resultPixels[i] = (diff > THRESHOLD) ? 0xFFFF0000 : 0xFF000000;
    }
    return Bitmap.createBitmap(resultPixels, width, height, Bitmap.Config.ARGB_8888);
}

优化要点：

动态阈值调整：根据环境光照自动计算阈值
三帧差分法：结合连续三帧消除重影
ROI区域聚焦：对特定区域进行重点检测

2.2 背景减除法实现

使用OpenCV的BackgroundSubtractorMOG2：

public Mat backgroundSubtraction(Mat frame) {
    Mat gray = new Mat();
    Mat foreground = new Mat();
    // 转换为灰度图
    Imgproc.cvtColor(frame, gray, Imgproc.COLOR_RGB2GRAY);
    // 初始化背景减除器（历史帧数=500，阈值=16）
    BackgroundSubtractorMOG2 bgSubtractor = 
        Video.createBackgroundSubtractorMOG2(500, 16, true);
    bgSubtractor.apply(gray, foreground);
    // 形态学操作去噪
    Mat kernel = Imgproc.getStructuringElement(
        Imgproc.MORPH_RECT, new Size(3, 3));
    Imgproc.morphologyEx(foreground, foreground, 
        Imgproc.MORPH_OPEN, kernel);
    return foreground;
}

参数调优建议：

历史帧数：300-800帧适应不同场景
阈值参数：8-25根据运动速度调整
阴影检测：开启可减少误检但增加计算量

2.3 光流法实现（Lucas-Kanade）

public List<Point> calculateOpticalFlow(Mat prevFrame, Mat currFrame) {
    Mat prevGray = new Mat();
    Mat currGray = new Mat();
    List<Point> prevPts = new ArrayList<>();
    List<Point> currPts = new ArrayList<>();
    // 转换为灰度图
    Imgproc.cvtColor(prevFrame, prevGray, Imgproc.COLOR_RGB2GRAY);
    Imgproc.cvtColor(currFrame, currGray, Imgproc.COLOR_RGB2GRAY);
    // 初始化特征点（使用GoodFeaturesToTrack）
    MatOfPoint corners = new MatOfPoint();
    Imgproc.goodFeaturesToTrack(prevGray, corners, 100, 0.01, 10);
    prevPts.addAll(corners.toList());
    // 计算光流
    MatOfPoint2f prevPts2f = new MatOfPoint2f(prevPts.toArray(new Point[0]));
    MatOfPoint2f currPts2f = new MatOfPoint2f();
    MatOfByte status = new MatOfByte();
    MatOfFloat err = new MatOfFloat();
    Video.calcOpticalFlowPyrLK(
        prevGray, currGray, prevPts2f, currPts2f, status, err);
    // 过滤有效点
    List<Point> result = new ArrayList<>();
    for (int i = 0; i < status.toArray().length; i++) {
        if (status.get(i, 0)[0] == 1) {
            result.add(currPts2f.get(i, 0)[0]);
        }
    }
    return result;
}

应用场景：

精确运动轨迹追踪
复杂背景下的物体运动分析
与特征点检测结合使用

三、性能优化策略

3.1 多线程处理架构

public class CameraProcessor {
    private HandlerThread processingThread;
    private Handler processingHandler;
    public void startProcessing() {
        processingThread = new HandlerThread("CameraProcessor");
        processingThread.start();
        processingHandler = new Handler(processingThread.getLooper());
    }
    public void processFrame(final Bitmap frame) {
        processingHandler.post(() -> {
            // 执行检测算法
            Bitmap result = detectMotion(frame);
            // 返回结果到主线程
            new Handler(Looper.getMainLooper()).post(() -> {
                updateUI(result);
            });
        });
    }
}

3.2 分辨率与帧率平衡

分辨率	适用场景	帧率范围	CPU占用
640x480	实时检测	25-30fps	15-20%
1280x720	高精度检测	15-20fps	30-40%
1920x1080	细节分析	8-12fps	50-60%

优化建议：

动态分辨率调整：根据设备性能自动选择
ROI裁剪：仅处理感兴趣区域
隔帧处理：每2-3帧处理一次

3.3 算法选择决策树

开始
│
├─ 实时性要求高？→ 是 → 帧差法
│   └─ 光照变化大？→ 是 → 三帧差分+自适应阈值
│   └─ 光照稳定 → 基本帧差法
│
├─ 精度要求高？→ 是 → 背景减除法
│   └─ 背景复杂？→ 是 → MOG2+阴影检测
│   └─ 背景简单 → KNN背景减除
│
└─ 轨迹分析需求？→ 是 → 光流法
    └─ 计算资源充足？→ 是 → Lucas-Kanade全图
    └─ 资源有限 → 特征点光流

四、完整实现示例

4.1 Camera2 API集成

public class CameraMotionDetector implements Camera.PreviewCallback {
    private Camera camera;
    private MotionDetectionAlgorithm detector;
    public void startDetection() {
        camera = Camera.open();
        Camera.Parameters params = camera.getParameters();
        params.setPreviewSize(640, 480);
        params.setPreviewFormat(ImageFormat.NV21);
        camera.setParameters(params);
        detector = new FrameDifferenceDetector(); // 或选择其他算法
        camera.setPreviewCallbackWithBuffer(this);
        camera.addCallbackBuffer(new byte[640*480*3/2]);
        camera.startPreview();
    }
    @Override
    public void onPreviewFrame(byte[] data, Camera camera) {
        // 转换YUV420到RGB
        YuvImage yuvImage = new YuvImage(data, ImageFormat.NV21, 640, 480, null);
        ByteArrayOutputStream os = new ByteArrayOutputStream();
        yuvImage.compressToJpeg(new Rect(0, 0, 640, 480), 100, os);
        Bitmap frame = BitmapFactory.decodeByteArray(os.toByteArray(), 0, os.size());
        // 执行检测
        Bitmap result = detector.detect(frame);
        // 显示结果
        runOnUiThread(() -> imageView.setImageBitmap(result));
        // 回收缓冲区
        camera.addCallbackBuffer(data);
    }
}

4.2 Jetpack CameraX实现

class CameraMotionDetectorActivity : AppCompatActivity() {
    private lateinit var imageAnalyzer: ImageAnalysis
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        setContentView(R.layout.activity_main)
        val cameraProviderFuture = ProcessCameraProvider.getInstance(this)
        cameraProviderFuture.addListener({
            val cameraProvider = cameraProviderFuture.get()
            imageAnalyzer = ImageAnalysis.Builder()
                .setTargetResolution(Size(640, 480))
                .setBackpressureStrategy(ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST)
                .build()
                .setAnalyzer(ContextCompat.getMainExecutor(this)) { image ->
                    val rotationDegrees = image.imageInfo.rotationDegrees
                    val bitmap = image.toBitmap()
                    // 执行检测
                    val result = MotionDetector.detect(bitmap)
                    // 显示结果
                    runOnUiThread { resultView.setImageBitmap(result) }
                }
            val preview = Preview.Builder().build()
            val cameraSelector = CameraSelector.DEFAULT_BACK_CAMERA
            try {
                cameraProvider.unbindAll()
                cameraProvider.bindToLifecycle(
                    this, cameraSelector, preview, imageAnalyzer
                )
            } catch (e: Exception) {
                Log.e(TAG, "Use case binding failed", e)
            }
        }, ContextCompat.getMainExecutor(this))
    }
}

五、常见问题解决方案

5.1 光照变化处理

动态阈值调整：

public int calculateAdaptiveThreshold(Bitmap frame) {
  int[] pixels = new int[frame.getWidth() * frame.getHeight()];
  frame.getPixels(pixels, 0, frame.getWidth(), 0, 0, 
                 frame.getWidth(), frame.getHeight());
  int sum = 0;
  for (int pixel : pixels) {
      int r = (pixel >> 16) & 0xFF;
      sum += r;
  }
  float avg = (float) sum / pixels.length;
  return (int) (avg * 0.2); // 根据平均亮度调整比例
}

5.2 硬件加速优化

RenderScript实现（适用于简单操作）：

public Bitmap rsThreshold(Bitmap input) {
  RenderScript rs = RenderScript.create(context);
  ScriptIntrinsicHistogram script = ScriptIntrinsicHistogram.create(rs);
  Allocation inAlloc = Allocation.createFromBitmap(rs, input);
  Allocation outAlloc = Allocation.createTyped(rs, inAlloc.getType());
  script.setInput(inAlloc);
  script.forEach(outAlloc);
  Bitmap output = Bitmap.createBitmap(input.getWidth(), 
                                    input.getHeight(), input.getConfig());
  outAlloc.copyTo(output);
  rs.destroy();
  return output;
}

5.3 多设备兼容性处理

设备特性	检测方法	适配方案
CPU核心数	Runtime.getRuntime().availableProcessors()	动态线程数调整
GPU支持	OpenCL/Vulkan可用性检测	启用硬件加速
摄像头能力	CameraCharacteristics.get(INFO_SUPPORTED_HARDWARE_LEVEL)	降级处理策略

六、进阶技术方向

6.1 深度学习集成

TensorFlow Lite模型优化：

try (Interpreter interpreter = new Interpreter(loadModelFile(context))) {
  Bitmap inputBitmap = Bitmap.createScaledBitmap(
      originalBitmap, 224, 224, true);
  ByteBuffer inputBuffer = convertBitmapToByteBuffer(inputBitmap);
  float[][] output = new float[1][NUM_DETECTIONS];
  interpreter.run(inputBuffer, output);
  // 解析输出结果
  DetectionResult result = parseOutput(output);
}

6.2 多摄像头协同检测

public class MultiCameraDetector {
    private CameraManager cameraManager;
    private Map<String, CameraCaptureSession> sessions = new HashMap<>();
    public void startDetection() {
        cameraManager = (CameraManager) context.getSystemService(Context.CAMERA_SERVICE);
        String[] cameraIds = cameraManager.getCameraIdList();
        for (String id : cameraIds) {
            CameraCharacteristics characteristics = 
                cameraManager.getCameraCharacteristics(id);
            if (isBackFacing(characteristics)) {
                openCamera(id);
            }
        }
    }
    private void openCamera(String cameraId) {
        try {
            cameraManager.openCamera(cameraId, 
                new CameraDevice.StateCallback() {
                    @Override
                    public void onOpened(@NonNull CameraDevice camera) {
                        createCaptureSession(camera);
                    }
                    // ...其他回调方法
                }, null);
        } catch (CameraAccessException e) {
            Log.e(TAG, "Camera access failed", e);
        }
    }
}

6.3 3D运动检测

双目视觉实现：

public class StereoVisionDetector {
  private Camera leftCamera;
  private Camera rightCamera;
  public float[] calculateDepth(Bitmap leftFrame, Bitmap rightFrame) {
      // 特征点匹配
      MatOfPoint2f leftPoints = detectFeatures(leftFrame);
      MatOfPoint2f rightPoints = detectFeatures(rightFrame);
      // 计算视差
      Mat disparity = new Mat();
      StereoBM stereo = StereoBM.create(64, 21);
      stereo.compute(convertToGray(leftFrame), 
                    convertToGray(rightFrame), disparity);
      // 转换为深度图
      float[] depthMap = new float[disparity.rows() * disparity.cols()];
      disparity.get(0, 0, depthMap);
      return depthMap;
  }
}

本文系统阐述了Android系统移动物体检测的全流程实现，从基础算法到高级优化提供了完整解决方案。实际开发中，建议根据具体场景（如实时监控、AR应用、运动分析等）选择合适的算法组合，并通过持续的性能调优达到最佳效果。随着设备性能的提升和AI技术的发展，移动端物体检测正朝着更高精度、更低功耗的方向演进，开发者需保持对新技术趋势的关注。

Android系统中移动物体检测步骤与方法深度解析