TLD算法ppt课件

1、.,Object Tracking with Tracking-Learning-Detection,.,Tracking-Learning-Detection,.,return,location,locations,Object location,.,Tracking-Learning-Detection,P-N Learning Object Model Object Detection Tracker Integrator Learning Component,.,PN-learning,A classifier to be learned training set a collecti

2、on of labeled training examples supervised training a method that trains a classifier from training set (iv) P-N experts,.,PN-learning,P-expert: analyzes examples classified as negative, estimates false negatives and adds them to training set with positive label. - N-expert: analyzes examples classi

3、fied as positive, estimates false positives and adds them with negative label to the training set. -,false positives: false negatives:,.,PN-learning,P-precision P-recall N-precision N-recall,.,PN-learning,After defining the state vector and a 2x2 matrix M as,It is possible to rewrite the equations a

4、s,.,PN-learning,The P-N experts are characterized by four quality measures, . To reduce this 4D space, the parameters are set to where represents error of the expert. The transition matrix then becomes . The eigenvalues of this matrix are . Therefore the P-N learning should be improving the performa

5、nce if . The error is varied in the range,.,PN-learning,.,PN-learning,The state vector converges to zero if both eigenvalues of the transition matrixMare smaller than one.,.,PN-learning (design of real experts),In every frame, the P-N learning performs the following steps:,evaluation of the detector

6、 on the current frame estimation of the detector errors using the P-N experts update of the detector by labeled examples output by the experts.,.,PN-learning (design of real experts),structure P-expert: assumes that the object moves along a trajectory. N-expert: assumes that the object can appear at

7、 a single location only.,.,PN-learning,The car is tracked from frame to frame by a tracker. The tracker represents the P-expert that outputs positive training examples.Notice that due to occlusion of the object, the output of P-expert in time t+2 outputs incorrect positive example. N-expert identifi

8、es maximally confident patch (denoted by a red star*) and labels all other detections as negative.,.,Tracking-Learning-Detection,P-N Learning Object Model Object Detection Tracker Integrator Learning Component,.,Object Model,Object model M is a data structure that represents the object and its surro

9、unding observed so far. Similarity between two patches is defined as,.,Object Model,Nearest Neighbor (NN) classifier A patch p is classified as positive if otherwise the patch is classified as negative. A classification margin is defined as,.,Object Model,Model update (i) the patchs label estimated

10、by NN classifier is different from the label given by the P-N experts. (ii) patches where the classification margin is smaller than .,.,Tracking-Learning-Detection,P-N Learning Object Model Object Detection Tracker Integrator Learning Component,.,Object Detector,Scanning-window grid scales step = 1.

11、2, horizontal step = 10% of width, vertical step = 10% of height, minimal bounding box size = 20 pixels Cascaded classifier,.,Object Detector,Patch variance,This stage rejects all patches, for which gray-value variance is smaller than 50% of variance of the patch that was selected for tracking.,The

12、stage exploits the fact that gray-value variance of a patch p can be expressed as , and that the expected value E(p) can be measured in constant time using integral images .,patch,0,y,x,.,Object Detector,Ensemble classifer,The posteriors of individual base classifiers are averaged and the ensemble c

13、lassifies the patch as the object if the average posterior is larger than 50%,.,Object Detector,Ensemble classifer,The ensemble consists of T base classifiers. Each base classifieri performs a number of pixel comparisons on the patch resulting in a binary code x.,.,Object Detector,Nearest neighbor c

14、lassifier,After filtering the patches by the variance filter and the ensemble classifier, we are typically left with several of bounding boxes that are not decided yet.Therefore, we can use the online model and classify the patch using a NN classifier. A patch is classified as the object if,.,Tracki

15、ng-Learning-Detection,P-N Learning Object Model Object Detection Tracker Integrator Learning Component,.,Tracker,Median-Flow(pyramidal Lucas-Kanade Tracker) The tracker estimates displacements of a number of points within the objects bounding box, estimates their reliability, and votes with 50% of t

16、he most reliable displacements for the motion of the bounding box using median. Failure detection,A residual of a single displacement is defined as . A failure of the tracker is declared if median pixel.,.,Tracking-Learning-Detection,P-N Learning Object Model Object Detection Tracker Integrator Lear

17、ning Component,.,Integrator,Integrator combines the bounding box of the tracker and the bounding boxes of the detector into a single bounding box output by TLD. If neither the tracker not the detector output a bounding box, the object is declared as not visible. Otherwise the integrator outputs the

18、maximally confident bounding box (using ) .,return,.,Tracking-Learning-Detection,P-N Learning Object Model Object Detection Tracker Integrator Learning Component,.,Learning component,Initialization P-expert N-expert,.,Learning component,.,Learning component,Initialization 1. the inital bounding box

19、10 bounding boxes 10 x20 positive patches 2. Negative patches are collected from the surrounding of the initializing bounding box,.,Learning component,Illustration of P-expert: a) object model and the core in feature space(gray blob), b) unreliable(dotted)and reliable(thick) trajectory, c) the object model and the core after the update.Red dots are positive examples, black dots are