OpenCV  3.4.1 Open Source Computer Vision
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...

Public Attributes inherited from cv::MultiTracker_Alt
std::vector< Rect2dboundingBoxes
Bounding Boxes list for Multi-Object-Tracker. More...

std::vector< Scalarcolors
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 [90] .

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.

Tracker, MultiTracker, TrackerTLD

## § 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)

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