Struct opencv::tracking::MultiTrackerTLD

pub struct MultiTrackerTLD { /* fields omitted */ }

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 TLD .

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

See also

Tracker, MultiTracker, TrackerTLD

Implementations

impl MultiTrackerTLD

pub fn as_raw_MultiTrackerTLD(&self) -> *const c_void

pub fn as_raw_mut_MultiTrackerTLD(&mut self) -> *mut c_void

Trait Implementations

impl Boxed for MultiTrackerTLD

unsafe fn from_raw(ptr: *mut c_void) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

fn as_raw_mut(&mut self) -> *mut c_void

Return the underlying mutable raw pointer

impl Drop for MultiTrackerTLD

fn drop(&mut self)

Executes the destructor for this type.

impl MultiTrackerTLDTrait for MultiTrackerTLD

fn as_raw_MultiTrackerTLD(&self) -> *const c_void

fn as_raw_mut_MultiTrackerTLD(&mut self) -> *mut c_void

fn update_opt(&mut self, image: &dyn ToInputArray) -> Result<bool>

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%

impl MultiTracker_AltTrait for MultiTrackerTLD

fn as_raw_MultiTracker_Alt(&self) -> *const c_void

fn as_raw_mut_MultiTracker_Alt(&mut self) -> *mut c_void

fn target_num(&self) -> i32

Current number of targets in tracking-list

fn set_target_num(&mut self, val: i32)

Current number of targets in tracking-list

fn trackers(&mut self) -> Vector<Ptr<dyn Tracker>>

Trackers list for Multi-Object-Tracker

fn set_trackers(&mut self, val: Vector<Ptr<dyn Tracker>>)

Trackers list for Multi-Object-Tracker

fn bounding_boxes(&mut self) -> Vector<Rect2d>

Bounding Boxes list for Multi-Object-Tracker

fn set_bounding_boxes(&mut self, val: Vector<Rect2d>)

Bounding Boxes list for Multi-Object-Tracker

fn colors(&mut self) -> Vector<Scalar>

List of randomly generated colors for bounding boxes display

fn set_colors(&mut self, val: Vector<Scalar>)

List of randomly generated colors for bounding boxes display

fn add_target(
    &mut self,
    image: &dyn ToInputArray,
    bounding_box: Rect2d,
    tracker_algorithm: Ptr<dyn Tracker>
) -> Result<bool>

Add a new target to a tracking-list and initialize the tracker with a known bounding box that surrounded the target ## Parameters * image: The initial frame * boundingBox: The initial bounding box of target * tracker_algorithm: Multi-tracker algorithm

fn update(&mut self, image: &dyn ToInputArray) -> Result<bool>

Update all trackers from the tracking-list, find a new most likely bounding boxes for the targets ## Parameters * image: The current frame

impl Send for MultiTrackerTLD