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通过运动模型判断是否稳跟。

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wangchongwu 3 months ago
parent ee96b4191c
commit af8d1a1b43
6 changed files with 64 additions and 21 deletions
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55
NeoTracker/src/Arith_TrackSAObj.cpp
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@ -1190,7 +1190,7 @@ SINT32 SA_Tracker::FindMatchTarget(PIPE* pPipe, TARGET_OBJECT* ptTargetArray, SI
//局部变量
FLOAT32 fSim = 0.0f; //目标相似度
FLOAT32 fSimMax = -1.0f;//目标相似度最大值
FLOAT32 fSimThres = 0.5f; //相似度阈值
FLOAT32 fSimThres = 0.3f; //相似度阈值
SINT32 nFrmsStep = 1; //帧间隔
TARGET_OBJECT* ptMainTarget = NULL; //主管道目标
TARGET_OBJECT* ptTarget = NULL; //候选目标
@ -1223,12 +1223,12 @@ SINT32 SA_Tracker::FindMatchTarget(PIPE* pPipe, TARGET_OBJECT* ptTargetArray, SI
}
// 小目标主要使用位置信息
// 小目标主要使用位置信息 //s3315运动不稳定，不优先使用运动模型
if (SizeType::SmallTarget == m_SizeMode)
{
fMoveModelWeight = 0.7f;
fApparentModelWeight = 0.3f;
fMoveThres = 0.7f;
fMoveModelWeight = 0.5f;
fApparentModelWeight = 0.5f;
fMoveThres = 0.5f;
}
// +++++++++++++++++++++++++++++++++++++++++++++++++++++
//若长短轨迹预测异常，则直接取距离搜索区域中心最近的目标
@ -1333,10 +1333,10 @@ SINT32 SA_Tracker::FindMatchTarget(PIPE* pPipe, TARGET_OBJECT* ptTargetArray, SI
fSScaleChangeHighThres = 3.f;
fAScaleChangeLowThres = 0.5;
fAScaleChangeHighThres = 2.0;
nLastDiffThresX = MAX(nMoveMin, 6);
nLastDiffThresY = MAX(nMoveMin, 6);
nPredictDiffThresX = MAX(nFThreMin, 6);
nPredictDiffThresY = MAX(nFThreMin, 6);
nLastDiffThresX = MAX(nMoveMin, 12);
nLastDiffThresY = MAX(nMoveMin, 12);
nPredictDiffThresX = MAX(nFThreMin, 12);
nPredictDiffThresY = MAX(nFThreMin, 12);
}
else if (pPipe->unLostCnt < 20)
{
@ -1391,11 +1391,19 @@ SINT32 SA_Tracker::FindMatchTarget(PIPE* pPipe, TARGET_OBJECT* ptTargetArray, SI
fSScaleChangeHighThres = 3.f;
fAScaleChangeLowThres = 0.5;
fAScaleChangeHighThres = 2.0;
nLastDiffThresX = MAX(nMoveMin, 4);
nLastDiffThresY = MAX(nMoveMin, 4);
nPredictDiffThresX = MAX(nFThreMin, 6);
nPredictDiffThresY = MAX(nFThreMin, 6);
nLastDiffThresX = MAX(nMoveMin, 12);
nLastDiffThresY = MAX(nMoveMin, 12);
nPredictDiffThresX = MAX(nFThreMin, 12);
nPredictDiffThresY = MAX(nFThreMin, 12);
}
//// 针对S3315大目标近距离伺服晃动，阈值强制放大
//else if(pPipe->ObjectFilter.sfSize.w >= 6 || pPipe->ObjectFilter.sfSize.h >= 6 || pPipe->ObjectFilter.fPxlsCnt > 9)
//{
// nLastDiffThresX = MAX(nLastDiffThresX, 36);
// nLastDiffThresY = MAX(nLastDiffThresY, 36);
// nPredictDiffThresX = MAX(nPredictDiffThresX, 36);
// nPredictDiffThresY = MAX(nPredictDiffThresY, 36);
//}
SINT32 nEnd = pPipe->ubEnd;
@ -1428,6 +1436,8 @@ SINT32 SA_Tracker::FindMatchTarget(PIPE* pPipe, TARGET_OBJECT* ptTargetArray, SI
}
// 目标运动距离强逻辑：用预测位置和上一帧的位置，防止跟踪跳转到错误的目标
if (nSimTargetId != -1)
{
@ -1465,6 +1475,19 @@ SINT32 SA_Tracker::FindMatchTarget(PIPE* pPipe, TARGET_OBJECT* ptTargetArray, SI
fSimMax = -1;
}
}
//// 灰度类型不同，任何情况都不允许跳跃
//if (ptTarget->fSNR * ptMainTarget->fSNR < 0)
//{
// if ((ABS(ptTarget->pfCenPos.x - stMotionMod_mean.crnObjPrediRtLong.cx) > MAX(ABS(fAzSpeed) * 2, 6)
// || ABS(ptTarget->pfCenPos.y - stMotionMod_mean.crnObjPrediRtLong.cy) > MAX(ABS(fPtSpeed) * 2, 6))
// && (ABS(ptTarget->pfCenPos.x - m_ObjStatus.ptPos.x) > MAX(ABS(fAzSpeed) * 2, 6)
// || ABS(ptTarget->pfCenPos.y - m_ObjStatus.ptPos.y) > MAX(ABS(fPtSpeed) * 2, 6)))
// {
// fSimMax = -1;
// }
//}
}
}
////+++++++++++++++++++++++++++++++++++++++++++++++++++++++
@ -1507,10 +1530,10 @@ SINT32 SA_Tracker::FindMatchTarget(PIPE* pPipe, TARGET_OBJECT* ptTargetArray, SI
}
// 警戒周边干扰时，调整阈值
if (pPipe->blookout)
if (pPipe->blookout && pPipe->stMotionMod_mean.bTrackStable)
{
fThres = 0.8 * ContiLostThres;
fMoveThres = 0.8 * 1.f - ContiLostThres;
fThres = fSimThres * ContiLostThres;
fMoveThres = fMoveThres * 1.f - ContiLostThres;
}

10
NeoTracker/src/Common/Arith_AglPredict.cpp
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@ -207,6 +207,16 @@ void Predict_ObjAglTrackPredict(FilterMeanNL* pFilter, SINT32 nWidth, SINT32 nHe
}
pFilter->dnObjPredictDist = dnNLDist;
// 预测偏离，或者滤波器被重置时，均不能认为跟踪稳定
if (dnNLDist.dx > 8 || dnNLDist.dy > 8 || (pFilter->ObjAglListsLong.nCnt == 0))
{
pFilter->bTrackStable = false;
}
else
{
pFilter->bTrackStable = true;
}
////利用长短时轨迹判断是否开启惯性预测,采用多帧判断 by wcw04046 @ 2022/01/17
//if (g_GLB_stOutput.nObjPredictFarCnt == 0)
//{

2
NeoTracker/src/Detect/Arith_DetectAreaObj.cpp
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@ -1825,7 +1825,7 @@ SINT32 DetectAreaObj::DAT_ObjsSegmentation(UINT16* pFrame, UBYTE8* pnGrayBinary,
//MSSu, 20150514: 弱目标检测时，灰度、梯度二值点取并集，否则取交集
BBOOL bSeedPt = FALSE;
if ((DAT_FLAG_BINARY == pnGrayBinary[nIndex]
|| DAT_FLAG_BINARY == pnGradBinary[nIndex])
&& DAT_FLAG_BINARY == pnGradBinary[nIndex])
&& DAT_FLAG_CONNECTED != pnFlagHasSr[nIndex])
{
bSeedPt = TRUE;

1
NeoTracker/src/NeoArithStandardDll.h
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@ -76,7 +76,6 @@ typedef struct tagARIDLL_OBJINFO
float fBgEntropy; //目标背景信息熵
float fSaliency; //目标显著性值
//
bool nJammingSucess; //目标成功干扰

2
NeoTracker/src/Version.h.in
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@ -3,4 +3,4 @@
#pragma once
#include <string>
std::string BUILD_TIME = "BUILD_TIME @build_time@";
std::string VERSION = "BUILD_VERSION 1.4.0";
std::string VERSION = "BUILD_VERSION 1.4.1";

13
QGuideArithStudio/src/QVideoPlayer.cpp
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@ -1924,10 +1924,21 @@ void QVideoPlayer::DrawPipeAglInfo()
auto TrackListInfo = ARIDLL_GetTrackerHistInfo(pEOTracker, P);
DrawAglList(m_ImageViewer, &TrackListInfo->ObjAglListsNear, QPen(QColor(255, 255, 255), 0.5, Qt::DotLine));
DrawAglList(m_ImageViewer, &TrackListInfo->ObjAglListsLong, QPen(QColor(255, 100, 100), 0.5, Qt::DotLine));
if (TrackListInfo->bTrackStable)
{
QPen pen1(QPen(QColor(255, 255, 255), 0.5, Qt::SolidLine));
DrawCrossInImage(m_ImageViewer, QRect(pPipe->stMotionMod_mean.crnObjPrediRtLong.cx - 40, pPipe->stMotionMod_mean.crnObjPrediRtLong.cy - 40, 80, 80), pen1);
DrawCrossInImage(m_ImageViewer, QRect(pPipe->stMotionMod_mean.crnObjPrediRtLong.cx - 80, pPipe->stMotionMod_mean.crnObjPrediRtLong.cy - 80, 160, 160), pen1);
}
else
{
QPen pen2(QPen(QColor(255, 255, 255), 0.5, Qt::DotLine));
DrawCrossInImage(m_ImageViewer, QRect(pPipe->stMotionMod_mean.crnObjPrediRtLong.cx - 40, pPipe->stMotionMod_mean.crnObjPrediRtLong.cy - 40, 80, 80), pen2);
}
QPen pen2(QPen(QColor(255, 20, 20), 0.5, Qt::SolidLine));
DrawCrossInImage(m_ImageViewer, QRect(pPipe->stMotionMod_mean.crnObjPrediRtNear.cx - 25, pPipe->stMotionMod_mean.crnObjPrediRtNear.cy - 25, 50, 50), pen2);
}
else if(bEnableDrawFrameDetect)
{

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