1.主跟踪目标后的背景管道输出抑制

2.四邻域提取取像素位置bug修复
3.默认打开面目标检测，增加跟踪稳定性
4.面目标检测灰度类型参数全局与局部保持一致
5.灰度分割灰度残差图无符号16位改为32位，避免溢出

NeoTracker/src/Arith_EOController.cpp

@@ -660,7 +660,7 @@ void Arith_EOController::Arith_SetRunTimeParam(ARIDLL_PARMA config)
     g_GLB_Detectors->SetParam(para);//设置参数
 
     // 全局检测器参数共享给局部检测器参数
-    //memcpy(&config.stSkyParam.prmTSkyDet, &config.PrmSkyDetect, sizeof(Param_SkyDetect));
+    memcpy(&config.stSkyParam.prmTSkyDet, &config.PrmSkyDetect, sizeof(Param_SkyDetect));
     
 
     // 设置管道实例参数
@@ -702,6 +702,11 @@ void Arith_EOController::Arith_SetRunTimeParam(ARIDLL_PARMA config)
     {
         LOG_SETLEVEL(config.nLogLevel);
     }
+
+    if (GLB_SCEN_MODE::GLB_SCEN_SKY == m_SceneType)
+    {
+        g_pSkyTracker->pSkyTracker->SetSkyParam(&g_GLB_stArithPara.stSkyParam);
+    }
 }
 
 // 读取运行时跟踪参数

NeoTracker/src/Arith_SkyTracker.cpp

@@ -537,9 +537,14 @@ void SkyTracker::SetSkyParam(Param_SkyTracker * stSkyParam)
     //mTrakingPara.Sky_bEnableTrackSA = stSkyParam->Sky_bEnableTrackSA;
     //mTrakingPara.Sky_bEnableKCF = stSkyParam->Sky_bEnableKCF;             // 内部自动调整关闭，因为清空了kcf信息，强制打开可能会导致程序崩溃，不建议设置kcf算法开关
 
+    if (pSATracker)
+    {
         pSATracker->m_TSA_Param.Sky_bUseAIDet = stSkyParam->Sky_bUseAIDet;
         pSATracker->m_TSA_Param.nUseAIDetFeq = stSkyParam->Sky_nUseAIDetFeq;
 
+        pSATracker->SetParam(stSkyParam->prmTSkyDet);//设置参数
+    }
+
     skyControlInfo.nTrackMemFrmNum = mTrakingPara.Sky_nTrackMemFrmNum;
 }
 
@@ -967,7 +972,7 @@ void SkyTracker::SetTrackModeAuto(GD_VIDEO_FRAME_S img, GLB_INPUT* p_GLB_Input)
             pKCFTracker->KCF_CleanUpObjectTracker();
 
             //清空CEND、KCF跟踪算法
-            memset(pObjStatusSA, 0, sizeof(OBJECTSTATUS));
+            //memset(pObjStatusSA, 0, sizeof(OBJECTSTATUS));
             memset(pObjStatusKCF, 0, sizeof(OBJECTSTATUS));
 
             //小目标和面目标相互切换计数器清零

NeoTracker/src/Arith_TrackSAObj.cpp

@@ -631,15 +631,16 @@ void SA_Tracker::SARegionDet(GD_VIDEO_FRAME_S img, GLB_INPUT* p_GLB_Input, SINT3
     //////////////////////////小面目标检测////////////////////////////////////
     BBOOL bEnableAreaObjDetect = FALSE;
     float fObjAglSpeed = ABS(m_LockingPipe->sfAglSpeed.vx) + ABS(m_LockingPipe->sfAglSpeed.vy);
-    // 像素数超过面目标检出下限或者速度较快，开启面目标检测算法
+    //// 像素数超过面目标检出下限或者速度较快，开启面目标检测算法
-    if ((m_ObjStatus.fObjPxlsCnt > DAT_TARGET_PXLS_MIN) || (fObjAglSpeed > 0.01f))
+    //if ((m_ObjStatus.fObjPxlsCnt > DAT_TARGET_PXLS_MIN) || (fObjAglSpeed > 0.01f))
-    {
+    //{
+    //    bEnableAreaObjDetect = true;
+    //}
+    //else
+    //{
+    //    bEnableAreaObjDetect = false;
+    //}
     bEnableAreaObjDetect = true;
-    }
-    else
-    {
-        bEnableAreaObjDetect = false;
-    }
     // 跟踪阶段，合并距离关键参数
     SINT32 nCombinDist = m_TSA_Input.nBlkWidth / 2 + 1;
     m_TSA_output.crCenterRect = m_TSA_Input.crCenterRect;
@@ -978,6 +979,15 @@ FLOAT32  SA_Tracker::cos_sim(POINT32F v1, POINT32F v2)
     return (fcosSim + 1) / 2.0f;
 }
 
+void SA_Tracker::SetParam(Param_SkyDetect para)
+{
+    pDST_Module->setDstDetState(para.bEnableDetcetSmallTarget);       // 小目标检测开关设置
+    pDST_Module->setDstParm(&para);                     // 小目标检测参数设置
+
+    pDAT_Module->setDatDetState(para.bEnableDetcetAreaTarget);       // 面目标检测开关设置
+    pDAT_Module->setDatParm(&para);                     // 面目标检测参数设置
+}
+
 // 运动相似度
 FLOAT32 SA_Tracker::Similarity_Move(PIPE* pPipe, TARGET_OBJECT* ptTarget, FLOAT32 fAglReso)
 {

NeoTracker/src/Arith_TrackSAObj.h

@@ -185,6 +185,8 @@ public:
     // 获取小目标检测模块
     API_DetectSmallObj* getDSTmodule();
 
+    // 检测器参数设置
+    void SetParam(Param_SkyDetect para);
 
     // 对空所有跟踪器统一输出，便于融合处理
     OBJECTSTATUS m_ObjStatus;

NeoTracker/src/Common/Arith_Bbox.cpp

@@ -223,6 +223,33 @@ FLOAT32 SIoUB(RECT16S A, RECT16S B)
     return IOUvalue;
 }
 
+FLOAT32 SIoUB_Expand(RECT16S A, RECT16S B, SINT32 expand)
+{
+    FLOAT32 IOUvalue = 0.0;
+    FLOAT32 IOUvalueA = 0.0;
+
+    SINT16 x1 = A.x - expand;
+    SINT16 y1 = A.y - expand;
+    SINT16 x2 = A.x + A.w + expand - 1;
+    SINT16 y2 = A.y + A.h + expand - 1;
+
+    SINT16 u1 = B.x - expand;
+    SINT16 v1 = B.y - expand;
+    SINT16 u2 = B.x + B.w + expand - 1;
+    SINT16 v2 = B.y + B.h + expand - 1;
+
+    FLOAT32 intersA = (FLOAT32)A.w * A.h;//目标A面积
+    FLOAT32 inters = MAX((MIN(x2, u2) - MAX(x1, u1)) + 1, 0.f) * MAX((MIN(y2, v2) - MAX(y1, v1)) + 1, 0.f);//交集
+    //float unions = (x2 - x1) * (y2 - y1) + (u2 - u1) * (v2 - v1) - inters;//并集
+    if (intersA > EPSILON)
+    {
+        IOUvalueA = 1.0f * inters / intersA;
+    }
+
+    IOUvalue = IOUvalueA;//交集占比最大值
+    return IOUvalue;
+}
+
 // 公共宽度占据B框宽度和高度的比例
 void Overlap_WH_Bratio(RECT32F A, RECT32F B, FLOAT32 *Overlap_Wb, FLOAT32 *Overlap_Hb)
 {

NeoTracker/src/Common/Arith_Bbox.h

@@ -31,6 +31,8 @@ FLOAT32 FIoUB(RECT32F A, RECT32F B);
 
 FLOAT32 SIoUB(RECT16S A, RECT16S B);
 
+FLOAT32 SIoUB_Expand(RECT16S A, RECT16S B, SINT32 expand);
+
 FLOAT32 IoUC(RECT32F A, RECT32F B);
 
 FLOAT32 IoUA32S(RECT32S *A, RECT32S *B);

NeoTracker/src/Detect/Arith_DetectAreaObj.cpp

@@ -238,7 +238,7 @@ void DetectAreaObj::DAT_MallocMemory(SINT32 nWidth, SINT32 nHeight, CENTERRECT m
     DAT_pBinary_Mag = new UBYTE8[DAT_CENTER_REGION_SIZE];
     DAT_pBinary_Gray = new UBYTE8[DAT_CENTER_REGION_SIZE];
     DAT_pBinary_GrayNew = new UBYTE8[DAT_CENTER_REGION_SIZE];
-    DAT_DIFF = new SINT16[DAT_CENTER_REGION_SIZE];
+    DAT_DIFF = new SINT32[DAT_CENTER_REGION_SIZE];
     DAT_pxDetectAreaGradIntegral = new SINT32[DAT_CENTER_REGION_SIZE];
 }
 
@@ -555,7 +555,7 @@ SINT32 DetectAreaObj::DAT_DetectAreaTarget(GD_VIDEO_FRAME_S img, GLB_STATUS nSta
 
     if (GLB_OBJ_GRAY_BRIGHT == m_DAT_stPara.nDetectGrayType)
     {
-        SINT16* pnTgBgDiff = (SINT16*)DAT_DIFF;
+        SINT32* pnTgBgDiff = (SINT32*)DAT_DIFF;
 
         //20161115，计算背景目标灰度残差图
         DAT_CalcTgBgDiff(m_DAT_stOutput.pnSrDSmpImg, snSrDSmp, m_DAT_stOutput.pnGrayInt,
@@ -622,7 +622,7 @@ SINT32 DetectAreaObj::DAT_DetectAreaTarget(GD_VIDEO_FRAME_S img, GLB_STATUS nSta
     }
     else if (GLB_OBJ_GRAY_DARK == m_DAT_stPara.nDetectGrayType)
     {
-        SINT16* pnTgBgDiff = (SINT16*)DAT_DIFF;
+        SINT32* pnTgBgDiff = (SINT32*)DAT_DIFF;
 
         //20161115，计算背景目标灰度残差图
         DAT_CalcTgBgDiff(m_DAT_stOutput.pnSrDSmpImg, snSrDSmp, m_DAT_stOutput.pnGrayInt,
@@ -690,7 +690,7 @@ SINT32 DetectAreaObj::DAT_DetectAreaTarget(GD_VIDEO_FRAME_S img, GLB_STATUS nSta
     {
         SINT32 nFrmBrightObjCnt = 0;
         SINT32 nFrmDarkObjCnt = 0;
-        SINT16* pnTgBgDiff = (SINT16*)DAT_DIFF;
+        SINT32* pnTgBgDiff = (SINT32*)DAT_DIFF;
 
         //20161112Whao，初始化亮暗目标数组
         TARGET_OBJECT* ptDATResultBright = (TARGET_OBJECT*)DAT_Target_Bright;
@@ -778,7 +778,7 @@ SINT32 DetectAreaObj::DAT_DetectAreaTarget(GD_VIDEO_FRAME_S img, GLB_STATUS nSta
     return nObjsCnt;
 }
 
-void dfs(unsigned char * grid, int nr, int nc, int r, int c)
+void dfs(unsigned char* grid, int nr, int nc, int r, int c)
 {
     grid[r * nc + c] = '\0';
     if (r - 1 >= 0 && grid[(r - 1) * nc + c] == '\x1') dfs(grid, nr, nc, r - 1, c);
@@ -787,7 +787,7 @@ void dfs(unsigned char * grid, int nr, int nc, int r, int c)
     if (c + 1 < nc && grid[r * nc + (c + 1)] == '\x1') dfs(grid, nr, nc, r, c + 1);
 }
 
-int numIslands(unsigned char *grid, int nr, int nc)
+int numIslands(unsigned char* grid, int nr, int nc)
 {
     int num_islands = 0;
     for (int r = 0; r < nr; ++r)
@@ -973,7 +973,7 @@ SINT32 DetectAreaObj::DAT_ObjectAreaSeg(GD_VIDEO_FRAME_S img, CENTERRECT crnSrRe
 
     if (GLB_OBJ_GRAY_BRIGHT == m_DAT_stPara.nDetectGrayType)
     {
-        SINT16* pnTgBgDiff = (SINT16*)DAT_DIFF;
+        SINT32* pnTgBgDiff = (SINT32*)DAT_DIFF;
 
         //20161115，计算背景目标灰度残差图
         DAT_CalcTgBgDiff(m_DAT_stOutput.pnSrDSmpImg, snSrDSmp, m_DAT_stOutput.pnGrayInt,
@@ -997,7 +997,7 @@ SINT32 DetectAreaObj::DAT_ObjectAreaSeg(GD_VIDEO_FRAME_S img, CENTERRECT crnSrRe
     }
     else if (GLB_OBJ_GRAY_DARK == m_DAT_stPara.nDetectGrayType)
     {
-        SINT16* pnTgBgDiff = (SINT16*)DAT_DIFF;
+        SINT32* pnTgBgDiff = (SINT32*)DAT_DIFF;
 
         //20161115，计算背景目标灰度残差图
         DAT_CalcTgBgDiff(m_DAT_stOutput.pnSrDSmpImg, snSrDSmp, m_DAT_stOutput.pnGrayInt,
@@ -1154,8 +1154,8 @@ void DetectAreaObj::DAT_CleanUpFrameDetectOutput(void)
     memset(ptTargetArrayCombine, 0, sizeof(TARGET_OBJECT) * DAT_TARGET_NUM_MAX);
 
     //20161116，初始化灰度残差图
-    SINT16* pnTgBgDiff = (SINT16*)DAT_DIFF;
+    SINT32* pnTgBgDiff = (SINT32*)DAT_DIFF;
-    memset(pnTgBgDiff, 0, sizeof(SINT16) * DAT_CENTER_REGION_SIZE);
+    memset(pnTgBgDiff, 0, sizeof(SINT32) * DAT_CENTER_REGION_SIZE);
 }
 
 
@@ -1387,7 +1387,7 @@ void DetectAreaObj::DAT_GradSegmentation(UINT16* pnImg, SIZE32S snImgSize,
 void DetectAreaObj::DAT_CalcTgBgDiff(UINT16* pFrame, SIZE32S snImgSize,
     SINT32* pnIntImg, SIZE32S snIntImgSize,
     SINT32 nRadius, SINT32 nRadiusTG,
-    SINT16* pnTgBgDiff)
+    SINT32* pnTgBgDiff)
 {
     SINT32  i, j;
     SINT32  nBorder = 1;    //不处理的边界大小
@@ -1585,7 +1585,7 @@ void DetectAreaObj::DAT_CalcTgBgDiff(UINT16* pFrame, SIZE32S snImgSize,
 *           4）输入图像(i,j)点，对应于积分图像上的(i+1,j+1)点。
 **********************************************************/
 void DetectAreaObj::DAT_GraySegmentation(SIZE32S snImgSize, SINT32 nRadius, SINT32 nThresMin,
-    UBYTE8* pnBinary, SINT16* pnTgBgDiff, BBOOL changeDetParam)
+    UBYTE8* pnBinary, SINT32* pnTgBgDiff, BBOOL changeDetParam)
 {
     SINT32  i, j;
     SINT32  nBorder = 1;    //不处理的边界大小

NeoTracker/src/Detect/Arith_DetectAreaObj.h

@@ -187,10 +187,10 @@ private:
     void    DAT_CalcTgBgDiff(UINT16* pFrame, SIZE32S snImgSize,
         SINT32* pnIntImg, SIZE32S snIntImgSize,
         SINT32 nRadius, SINT32 nRadiusTG,
-        SINT16* pnTgBgDiff);
+        SINT32* pnTgBgDiff);
 
     //      灰度分割
-    void    DAT_GraySegmentation(SIZE32S snImgSize, SINT32 nRadius, SINT32 nThresMin, UBYTE8* pnBinary, SINT16* pnTgBgDiff, BBOOL changeDetParam);
+    void    DAT_GraySegmentation(SIZE32S snImgSize, SINT32 nRadius, SINT32 nThresMin, UBYTE8* pnBinary, SINT32* pnTgBgDiff, BBOOL changeDetParam);
 
     //      目标分割（在2倍降采样图像上检测目标，再还原回原始图像位置及大小）
     SINT32  DAT_ObjsSegmentation(UINT16* pFrame, UBYTE8* pnGrayBinary,
@@ -263,7 +263,7 @@ private:
     UBYTE8*        DAT_pBinary_Gray;				//DAT: 灰度二值化图像（2倍降采样）  //BYTES: (320/2)*(256/2)*1 = 20480 - 0x5000
     UBYTE8*        DAT_pBinary_GrayNew;				//DAT: 灰度二值化图像（1倍降采样）  //BYTES: (320/2)*(256/2)*1 = 20480 - 0x5000
     MINMAXRECT32S DAT_pmrnBlksRect[DAT_SR_RGN_BLKS_NUM];					//DAT: 分块区域起始坐标数组（2倍降采样）
-    SINT16*        DAT_DIFF;							//DAT: 20161116，面目标灰度残差图
+    SINT32*        DAT_DIFF;							//DAT: 20161116，面目标灰度残差图
     //++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
     SINT16          GLB_Dat16SampleCnt = 0;	//目标16倍下采样的帧连续计数器
     SINT16          GLB_Dat8SampleCnt = 0;	//目标8倍下采样的帧连续计数器

NeoTracker/src/Detect/Arith_DetectSmallObj.cpp

@@ -698,7 +698,6 @@ void DetectSmallObj::DST_MallocMemory(SINT32 nWidth, SINT32 nHeight, CENTERRECT
     DST_pBlkPtBeTargetFlag = new SINT32[DST_BLK_NUM * 2];
 
     DST_pBlkMaxPntFlag = new BYTE8[DST_BLK_NUM * 2];
-
 }
 
 void DetectSmallObj::DST_ReleaseMemory()
@@ -2101,8 +2100,8 @@ BBOOL DetectSmallObj::DST_PipeTargetReDetect(GD_VIDEO_FRAME_S img, SINT32 nWidth
                 nIndex = pBlkStart + j;
                 int nnIndex_left = nIndex - 1;
                 int nnIndex_right = nIndex + 1;
-                int nnIndex_top = nIndex - nWidth;
+                int nnIndex_top = MAX(0, nIndex - nWidth);
-                int nnIndex_bottom = nIndex + nWidth;
+                int nnIndex_bottom = MIN(nIndex + nWidth, nHeight - 1);
 
                 if (GD_PIXEL_FORMAT_GRAY_Y8 == img.enPixelFormat)
                 {

NeoTracker/src/NeoArithStandardDll.cpp

@@ -454,7 +454,7 @@ void ARIDLL_OutputPipeTarget(Arith_EOController* pArith, ARIDLL_OUTPUT* pstOutpu
         pipeBox.w = pTarget->snSize.w;
         pipeBox.h = pTarget->snSize.h;
 
-        if(SIoUB(pipeBox,trackBox) > 0.2 && !pPipe->bTrackingPipe)
+        if(SIoUB_Expand(pipeBox,trackBox, 10) > 0.1 && !pPipe->bTrackingPipe)
         {
             continue;
         }