Functions
void	getDetectorBboxCorrelationResponses (const cudaStream_t &stream, const int num_of_targets, const int maxTargetsPerStream, const detectorBbox_t detectedPts, const uint detectedPts_size, const int detCorrRespSearchWindowSize, const float corr_responses, const float maxCorrResponse_ma, const int img_element_size, const int img_width, float confidenceAtDetectedPt, float corrRespAtDetectedPt)
	Read the correlation response near each detector bbox and write the confidence and response. More...

void	applyFilter2D_U16 (const cudaStream_t &stream, const uint16_t ftrs, const float masking_window, const uint srcWidth, const uint srcPitch, const uint srcHeight, const uint2 outBatchingGrid, const uint num_of_channels, const uint num_of_targets, const bool useColorNames, const uint num_of_colorNames_channels, float *ftrs_masked)
	Multiply a 2D high-precision feature by a masking window elementwise. More...

void	applyFilter2D_U8 (const cudaStream_t &stream, const uchar ftrs, const float masking_window, const uint srcWidth, const uint srcPitch, const uint srcHeight, const uint2 outBatchingGrid, const uint num_of_channels, const uint num_of_targets, const bool useColorNames, const uint num_of_colorNames_channels, float *ftrs_masked)
	Multiply a 2D low-precision feature by a masking window elementwise. More...

void	createDcfFilter (const cudaStream_t &stream, const cufftHandle &cufft_handle_C2R, const cufftHandle &cufft_handle_R2C, const cuComplex ftrs_F, const cuComplex desiredResp_F, const float masking_window_model, const int img_width, const int img_element_size, const int batch_element_size, const float lambda, const int num_of_channels, const int num_of_targets, cuComplex work_F, float work_I, const uint32_t tracker_age, const bool trackerDoProcessing_d, const float learn_rate, cuComplex learned_filter_F, cuComplex *result_filter_F)
	Create a DCF filter and update the learned filter with an exponential moving average. More...

void	createSegmentationFilterMask (const cudaStream_t &stream, const float src_mask_frame_batch, const int src_frame_width, const int src_frame_element_size, const int feat_width, const int feat_element_size, const int batch_element_size, const int num_of_targets, const int num_of_channels, const float4 src_rois, const bool trackerDoProcessing_d, float dst_mask_patch_batch)
	Crop and rescale each target's segmentation mask to the visual tracker region size. More...

void	computeChannelWeights (const cudaStream_t &stream, const cufftHandle &cufft_handle_many, const cuComplex ftrs_F, const int img_element_size, const int img_width, const int batch_element_size, const int batch_size, const int num_of_channels, const int num_of_targets, const cuComplex new_filter_F, cuComplex work_F, float work_I, const uint32_t tracker_age, const bool trackerDoProcessing_d, const float weights_lr, float channel_weights, float weight_sum)
	Compute and update the channel weights via exponential moving average. More...

void	calcuateExpectedPeakCorrelation (const cudaStream_t &stream, const int num_of_channels, const int num_of_targets, const float learn_rate, const float channel_weights, const uint32_t tracker_age, const bool trackerDoProcessing_d, float maxCorrResponse_ma)
	Compute the moving average of the max correlation response. More...

void	calculateResponse (const cudaStream_t &stream, const cufftHandle &cufft_handle_many_small, const int img_width, const int img_element_size, const int batch_element_size, const int num_of_channels, const int num_of_targets, const cuComplex ftrs_F, const cuComplex result_filter_F, const float channel_weights, cuComplex work_F, float corr_responses, float maxResp, float2 *targetDisplacement)
	Given the Fourier-transformed extracted features and the learned filters, compute the correlation response. More...

void	calculateTrackingConfidence (const cudaStream_t &stream, const int num_of_targets, const float maxCorrResponse_ma, const float maxCorrResponse, float *trackingConfidence)
	Calculate tracking confidence from moving-average and instantaneous peak correlation responses. More...

Function Documentation

◆ applyFilter2D_U16()

void cuDCF::applyFilter2D_U16	(	const cudaStream_t &	stream,
		const uint16_t *	ftrs,
		const float *	masking_window,
		const uint	srcWidth,
		const uint	srcPitch,
		const uint	srcHeight,
		const uint2	outBatchingGrid,
		const uint	num_of_channels,
		const uint	num_of_targets,
		const bool	useColorNames,
		const uint	num_of_colorNames_channels,
		float *	ftrs_masked
	)

Multiply a 2D high-precision feature by a masking window elementwise.

Parameters

[in]	stream	CUDA stream.
[in]	ftrs	The input high-precision feature.
[in]	masking_window	The filter multiplied with the feature.
[in]	srcWidth	The width of the input feature.
[in]	srcPitch	The pitch of the input feature.
[in]	srcHeight	The height of the input feature.
[in]	outBatchingGrid	The 2D size of the VPI feature grid.
[in]	num_of_channels	The number of channels in a feature vector.
[in]	num_of_targets	The number of targets processed in this batch.
[in]	useColorNames	Whether ColorNames features are included.
[in]	num_of_colorNames_channels	The number of leading ColorNames channels.
[out]	ftrs_masked	The feature after the filter is applied.

◆ applyFilter2D_U8()

void cuDCF::applyFilter2D_U8	(	const cudaStream_t &	stream,
		const uchar *	ftrs,
		const float *	masking_window,
		const uint	srcWidth,
		const uint	srcPitch,
		const uint	srcHeight,
		const uint2	outBatchingGrid,
		const uint	num_of_channels,
		const uint	num_of_targets,
		const bool	useColorNames,
		const uint	num_of_colorNames_channels,
		float *	ftrs_masked
	)

Multiply a 2D low-precision feature by a masking window elementwise.

Parameters

[in]	stream	CUDA stream.
[in]	ftrs	The input low-precision feature.
[in]	masking_window	The filter multiplied with the feature.
[in]	srcWidth	The width of the input feature.
[in]	srcPitch	The pitch of the input feature.
[in]	srcHeight	The height of the input feature.
[in]	outBatchingGrid	The 2D size of the VPI feature grid.
[in]	num_of_channels	The number of channels in a feature vector.
[in]	num_of_targets	The number of targets processed in this batch.
[in]	useColorNames	Whether ColorNames features are included.
[in]	num_of_colorNames_channels	The number of leading ColorNames channels.
[out]	ftrs_masked	The feature after the filter is applied.

◆ calcuateExpectedPeakCorrelation()

void cuDCF::calcuateExpectedPeakCorrelation	(	const cudaStream_t &	stream,
		const int	num_of_channels,
		const int	num_of_targets,
		const float	learn_rate,
		const float *	channel_weights,
		const uint32_t *	tracker_age,
		const bool *	trackerDoProcessing_d,
		float *	maxCorrResponse_ma
	)

Compute the moving average of the max correlation response.

Parameters

[in]	stream	CUDA stream.
[in]	num_of_channels	The number of channels in a feature vector.
[in]	num_of_targets	The number of targets processed in this batch.
[in]	learn_rate	The learning rate for the moving average.
[in]	channel_weights	The adaptive filter weights for the channels.
[in]	tracker_age	The age of each tracker.
[in]	trackerDoProcessing_d	Whether to process each target.
[in,out]	maxCorrResponse_ma	The moving-averaged max correlation response.

◆ calculateResponse()

void cuDCF::calculateResponse	(	const cudaStream_t &	stream,
		const cufftHandle &	cufft_handle_many_small,
		const int	img_width,
		const int	img_element_size,
		const int	batch_element_size,
		const int	num_of_channels,
		const int	num_of_targets,
		const cuComplex *	ftrs_F,
		const cuComplex *	result_filter_F,
		const float *	channel_weights,
		cuComplex *	work_F,
		float *	corr_responses,
		float *	maxResp,
		float2 *	targetDisplacement
	)

Given the Fourier-transformed extracted features and the learned filters, compute the correlation response.

Parameters

[in]	stream	CUDA stream.
[in]	cufft_handle_many_small	The smaller cuFFT handle used for one correlation response per target.
[in]	img_width	The feature-matrix width.
[in]	img_element_size	The number of elements in one feature matrix.
[in]	batch_element_size	The number of feature-vector elements for all current trackers.
[in]	num_of_channels	The number of channels in a feature vector.
[in]	num_of_targets	The number of targets processed in this batch.
[in]	ftrs_F	The extracted features in Fourier space.
[in]	result_filter_F	The moving-averaged DCF filter in Fourier space.
[in]	channel_weights	The adaptive filter weights for the channels.
[out]	work_F	A temporary workspace for complex arrays.
[out]	corr_responses	The resulting correlation response in image space.
[out]	maxResp	The peak response values.
[out]	targetDisplacement	The target displacement from the center to the peak correlation response.

◆ calculateTrackingConfidence()

void cuDCF::calculateTrackingConfidence	(	const cudaStream_t &	stream,
		const int	num_of_targets,
		const float *	maxCorrResponse_ma,
		const float *	maxCorrResponse,
		float *	trackingConfidence
	)

Calculate tracking confidence from moving-average and instantaneous peak correlation responses.

Parameters

[in]	stream	CUDA stream.
[in]	num_of_targets	The number of targets processed in this batch.
[in]	maxCorrResponse_ma	The moving-averaged max correlation response.
[in]	maxCorrResponse	The instantaneous max correlation response.
[out]	trackingConfidence	The resulting tracking confidence.

◆ computeChannelWeights()

void cuDCF::computeChannelWeights	(	const cudaStream_t &	stream,
		const cufftHandle &	cufft_handle_many,
		const cuComplex *	ftrs_F,
		const int	img_element_size,
		const int	img_width,
		const int	batch_element_size,
		const int	batch_size,
		const int	num_of_channels,
		const int	num_of_targets,
		const cuComplex *	new_filter_F,
		cuComplex *	work_F,
		float *	work_I,
		const uint32_t *	tracker_age,
		const bool *	trackerDoProcessing_d,
		const float	weights_lr,
		float *	channel_weights,
		float *	weight_sum
	)

Compute and update the channel weights via exponential moving average.

Parameters

[in]	stream	CUDA stream.
[in]	cufft_handle_many	The cuFFT handler for batch processing.
[in]	ftrs_F	The extracted features in Fourier space.
[in]	img_element_size	The number of elements in one feature matrix.
[in]	img_width	The feature-matrix width.
[in]	batch_element_size	The number of feature-vector elements for all current trackers.
[in]	batch_size	The number of feature matrices across channels and target trackers.
[in]	num_of_channels	The number of channels in a feature vector.
[in]	num_of_targets	The number of targets processed in this batch.
[in]	new_filter_F	The newly created DCF filter in Fourier space.
[out]	work_F	A temporary workspace for complex arrays.
[out]	work_I	A temporary workspace for float arrays.
[in]	tracker_age	The age of each tracker.
[in]	trackerDoProcessing_d	Whether to process each target.
[in]	weights_lr	The learning rate for the moving average of channel weights.
[in,out]	channel_weights	The adaptive filter weights for the channels.
[out]	weight_sum	Filter-weight sums used as workspace during normalization.

◆ createDcfFilter()

void cuDCF::createDcfFilter	(	const cudaStream_t &	stream,
		const cufftHandle &	cufft_handle_C2R,
		const cufftHandle &	cufft_handle_R2C,
		const cuComplex *	ftrs_F,
		const cuComplex *	desiredResp_F,
		const float *	masking_window_model,
		const int	img_width,
		const int	img_element_size,
		const int	batch_element_size,
		const float	lambda,
		const int	num_of_channels,
		const int	num_of_targets,
		cuComplex *	work_F,
		float *	work_I,
		const uint32_t *	tracker_age,
		const bool *	trackerDoProcessing_d,
		const float	learn_rate,
		cuComplex *	learned_filter_F,
		cuComplex *	result_filter_F
	)

Create a DCF filter and update the learned filter with an exponential moving average.

Parameters

[in]	stream	CUDA stream.
[in]	cufft_handle_C2R	The cuFFT C2R handler for batch processing.
[in]	cufft_handle_R2C	The cuFFT R2C handler for batch processing.
[in]	ftrs_F	The extracted features in Fourier space.
[in]	desiredResp_F	The desired response in Fourier space.
[in]	masking_window_model	The masking window for focused windowing in image space.
[in]	img_width	The feature-matrix width.
[in]	img_element_size	The number of elements in one feature matrix.
[in]	batch_element_size	The number of feature-vector elements for all current trackers.
[in]	lambda	The regularization constant used in the ridge-regression solver.
[in]	num_of_channels	The number of channels in a feature vector.
[in]	num_of_targets	The number of targets processed in this batch.
[out]	work_F	A temporary workspace for complex arrays.
[out]	work_I	A temporary workspace for float arrays.
[in]	tracker_age	The age of each tracker.
[in]	trackerDoProcessing_d	Whether to process each target.
[in]	learn_rate	The learning rate for the moving average of DCF filters.
[out]	learned_filter_F	The newly created DCF filter.
[in,out]	result_filter_F	The moving-averaged DCF filter in Fourier space.

◆ createSegmentationFilterMask()

void cuDCF::createSegmentationFilterMask	(	const cudaStream_t &	stream,
		const float *	src_mask_frame_batch,
		const int	src_frame_width,
		const int	src_frame_element_size,
		const int	feat_width,
		const int	feat_element_size,
		const int	batch_element_size,
		const int	num_of_targets,
		const int	num_of_channels,
		const float4 *	src_rois,
		const bool *	trackerDoProcessing_d,
		float *	dst_mask_patch_batch
	)

Crop and rescale each target's segmentation mask to the visual tracker region size.

Parameters

[in]	stream	CUDA stream.
[in]	src_mask_frame_batch	The source mask from the segmenter output.
[in]	src_frame_width	The width of the source mask.
[in]	src_frame_element_size	The number of pixels in the source mask.
[in]	feat_width	The destination filter width.
[in]	feat_element_size	The number of elements in the destination filter.
[in]	batch_element_size	The number of filter elements across all channels and supported targets.
[in]	num_of_targets	The number of targets processed in this batch.
[in]	num_of_channels	The number of channels in a feature vector.
[in]	src_rois	Each target's region in segmenter-output coordinates.
[in]	trackerDoProcessing_d	Whether to process each target.
[out]	dst_mask_patch_batch	The cropped and rescaled destination masks.

◆ getDetectorBboxCorrelationResponses()

void cuDCF::getDetectorBboxCorrelationResponses	(	const cudaStream_t &	stream,
		const int	num_of_targets,
		const int	maxTargetsPerStream,
		const detectorBbox_t *	detectedPts,
		const uint *	detectedPts_size,
		const int	detCorrRespSearchWindowSize,
		const float *	corr_responses,
		const float *	maxCorrResponse_ma,
		const int	img_element_size,
		const int	img_width,
		float *	confidenceAtDetectedPt,
		float *	corrRespAtDetectedPt
	)

Read the correlation response near each detector bbox and write the confidence and response.

Parameters

[in]	stream	CUDA stream.
[in]	num_of_targets	The number of targets processed in this batch.
[in]	maxTargetsPerStream	The maximum number of targets in each stream.
[in]	detectedPts	Detector bboxes in correlation-response coordinates.
[in]	detectedPts_size	The number of detector bboxes for each target.
[in]	detCorrRespSearchWindowSize	The search radius around each detector point in the correlation response.
[in]	corr_responses	The correlation response after applying the filter.
[in]	maxCorrResponse_ma	The moving-averaged max correlation response.
[in]	img_element_size	The number of elements in one feature matrix.
[in]	img_width	The feature-matrix width.
[out]	confidenceAtDetectedPt	The tracking confidence at each detector bbox.
[out]	corrRespAtDetectedPt	The correlation response at each detector bbox.

NVIDIA DeepStream SDK API Reference

9.2 Release

Functions

Function Documentation

◆ applyFilter2D_U16()

◆ applyFilter2D_U8()

◆ calcuateExpectedPeakCorrelation()

◆ calculateResponse()

◆ calculateTrackingConfidence()

◆ computeChannelWeights()

◆ createDcfFilter()

◆ createSegmentationFilterMask()

◆ getDetectorBboxCorrelationResponses()