cv::MultiTrackerTLD Class Reference

Multi Object Tracker for TLD. TLD is a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection. More...

#include "tracker.hpp"

Inheritance diagram for cv::MultiTrackerTLD:

Public Member Functions
bool	update_opt (InputArray image)
	Update all trackers from the tracking-list, find a new most likely bounding boxes for the targets by optimized update method using some techniques to speedup calculations specifically for MO TLD. The only limitation is that all target bounding boxes should have approximately same aspect ratios. Speed boost is around 20%. More...
Public Member Functions inherited from cv::MultiTracker_Alt
	MultiTracker_Alt ()
	Constructor for Multitracker. More...
bool	addTarget (InputArray image, const Rect2d &boundingBox, Ptr< Tracker > tracker_algorithm)
	Add a new target to a tracking-list and initialize the tracker with a known bounding box that surrounded the target. More...
bool	update (InputArray image)
	Update all trackers from the tracking-list, find a new most likely bounding boxes for the targets. More...

Additional Inherited Members
Public Attributes inherited from cv::MultiTracker_Alt
std::vector< Rect2d >	boundingBoxes
	Bounding Boxes list for Multi-Object-Tracker. More...
std::vector< Scalar >	colors
	List of randomly generated colors for bounding boxes display. More...
int	targetNum
	Current number of targets in tracking-list. More...
std::vector< Ptr< Tracker > >	trackers
	Trackers list for Multi-Object-Tracker. More...

Detailed Description

Multi Object Tracker for TLD. TLD is a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection.

The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates detector's errors and updates it to avoid these errors in the future. The implementation is based on [100] .

The Median Flow algorithm (see cv::TrackerMedianFlow) was chosen as a tracking component in this implementation, following authors. Tracker is supposed to be able to handle rapid motions, partial occlusions, object absence etc.

See also

Tracker, MultiTracker, TrackerTLD

Member Function Documentation

update_opt()

bool cv::MultiTrackerTLD::update_opt

(

InputArray

image

)

Update all trackers from the tracking-list, find a new most likely bounding boxes for the targets by optimized update method using some techniques to speedup calculations specifically for MO TLD. The only limitation is that all target bounding boxes should have approximately same aspect ratios. Speed boost is around 20%.

Parameters

image

The current frame.

Returns

True means that all targets were located and false means that tracker couldn't locate one of the targets in current frame. Note, that latter does not imply that tracker has failed, maybe target is indeed missing from the frame (say, out of sight)

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The documentation for this class was generated from the following file:

• /build/master-contrib_docs-lin64/opencv_contrib/modules/tracking/include/opencv2/tracking/tracker.hpp