Visual Object Tracking Based on Local Steering Kernels and Color Histograms IEEE TRANSACTIONS ON...

Visual Object Tracking Based on Local Steering Kernels and Color Histograms

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGYVOL. 23, NO.5, MAY 2013

Olga Zoidi, Anastasios Tefas, Member, IEEE

Ioannis Pitas, Fellow, IEEE

Overview

• Introduction

• Proposed method

• Experiment Result

• Conclusion

Overview

• Introduction

Visual tracking

Object representation

Object position prediction

• Proposed method

• Experiment Result

• Conclusion

Visual Tracking

• Visual tracking is difficult to

accomplish as some reason

Illumination conditions

Object may be nonrigid or

articulated

Occluded

Rapid and complicated

movements

Overview

• Introduction

Visual tracking

Object representation

Object position prediction

• Proposed method

• Experiment Result

• Conclusion

Object Representation

• Model-based

• Appearance-based

• Contour-based

• Feature-based

• Hybrid

Object Representation : Model-Based

• Exploit a priori information about the object shape and create model [7].

• Deal with the problem of object tracking under illumination variations, viewing angle, and partial

occlusion.

• Heavy cost.

[7]D.Roller, etc, “Model-based object tracking in monocular image sequences of road traffic scenes” Int. J. Comput. Vision, vol. 10, pp. 257-281, Mar.1993.

Object Representation : Appearance-Based

• Use the visual information for the object projection on the image plane, i.e., color, texture, and

shape.

• Deal with simple object transformation.

• Sensitive to illumination changes.

Object Representation : Contour-Based

• By employing shape matching or contour-evolution techniques [9]. Contour can be represented by

active models, such as snakes or B-splines [10].

• Deal with rigid and nonrigid objects.

• Incorporate with occlusion detection and estimation techniques.

[9] A. Yilmaz, X.Li, and M. Shah, “Contour-based object tracking with occlusion handling in video acquired using mobile cameras”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 11, pp. 1531-1536, Nov. 2004[10] Y.Wang and O. Lee, “Active mesh – a feature seeking and tracking image sequence representation scheme”, IEEE Trans, Image Process, vol.3, no. 5, pp. 610-624, Sep. 1994

Object Representation : Feature-Based

• By tracking a set of feature points and these features are then grouped.

• Problem is the correct distinction between the target object and background features.

Overview

• Introduction

Visual tracking

Object representation

Object position prediction

• Proposed method

• Experiment Result

• Conclusion

Object Position Prediction

• The position of the object in the following frame is usually predicted using a linear Kalman filter.

[32]

[32] G. Welch and G. Bishop, “An introduction to the Kalman filter,” Univ. North Carolina, Chapel Hill, NC, Tech. Rep. TR95041, 2000.

Overview

• Introduction

• Proposed method

• Experiment Result

• Conclusion

Proposed Method

• Some tips of object tracking algorithm

Using color histogram (CH) to handle severe change in the object view.

Using decomposing target into fragments, which are tracked separately, to handle partial occlusion.

Using local steering kernel (LSK) object texture descriptor to represent the region of interest (ROI).

• Proposed tracking approach is an appearance based method using both CHs and LSK descriptor.

Proposed Method

• First, search image regions in video frame that have high color similarity to the object CH, and get

candidate regions.

• Next, LSK descriptors of both the target object and candidate search regions are extracted.

• Discard the image regions with small CH similarity to the object CH, the new position of the object

is selected as the image region, whose LSK representation has the maximum similarity to the one

of the target object.

• As tracking evolves, target object appearance changes and being a stack containing different

instances. Stack is updated with the representation of the most recent detected object.

LSK Object Tracing Framework

• Steps

A. Initialization of the object ROI in the first video frame. Initialization can be done manually.

B. Use CH information for color similarity search in the current search region. CH information can lead to

background subtraction and reduction of the number of the candidate.

C. Representation of both the object and search region with LSK features.

D. Decision on the object ROI in the new frame, based on the similarities between candidate and: a) ROI

in the previous frame, and b) top object instance in the stack.

E. Update the object instance in the stack.

F. Prediction of the object position in the following video frame and initialization of an object search

region.

Overview

• Introduction

• Proposed method

Color Similarity

Object Texture Description

Object Localization and Model Update

Search Region Extraction in The Next Frame

• Experiment Result

• Conclusion

Color Similarity

• After object position prediction and search region selection, the search region of size R1*R2 is

divided into candidate ROI which size is Q1*Q2.

• Parameter d determines a uniform sampling of the candidate object ROIs every d pixels in the

search region.

• At frame t, the Bt% of the search region patches with the minimal histogram similarity to the

object histogram are considered to belong to the background.

• Cosine similarity :

• Normalized form S =

Color Similarity

• Three color channels’ S of all patches comprise a matrix MCH.

• The distribution of MCH takes values and sets a threshold in deciding whether the patch is a valid

candidate ROI.

• Finally, the binary matrix BCH, whose entry is set to 1 if entry of MCH is ≧threshold and 0, otherwise.

BCH will be used in tracking in Object Location section.

• Setting , and as the mean, maximal, and minimal values of entries, respectively.

•

Overview

• Introduction

• Proposed method

Color Similarity

Object Texture Description

Object Localization and Model Update

Search Region Extraction in The Next Frame

• Experiment Result

• Conclusion

Object Texture Description

• Introduction of LSK descriptors

LSKs are descriptors of the image salient features.

They were proven to be robust in small scale and orientation changes and deformations.

Result in successful tracking of slowly deformable objects.

• LSKs descriptors are a nonlinear combination of weighted spatial distances between a pixel p of

an image of size N1*N2 and its surrounding M*M pixels (pi). (M is equal to 3 pixels in this paper)

• The distance K is measured using a weighted Euclidean distance, which uses as weights the

covariance matrix Ci of the image gradients.

Object Texture Description

• In order to get Ci matrix in Ki(p), get gradient vectors gi and formed matrix GiM^2*2. Where .

• And Ci can be calculated via the singular value decomposition (SVD) of Gi.

• , and

Object Texture Description

• For each neighboring pixel , ,extract K(p) and normalize into , where is the L1-norm.

• Above concepts are applied. First converted ROI and search region from RGB to La*b* color space

and the LSKs are computed for each channel separately through steps above.

• The final representation of ROI is obtained by applying PCA [26].

• Finally, the search region is divided into patches and the LSK similarity matrix, which will be used

in next section, is estimated (like color similarity) by applying the cosine similarity measure.

[26] H. Seo and P. Milanfar, “Training-free, generic object detection using locally adaptive regression kernels,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1688–1704, Sep. 2010.

Overview

• Introduction

• Proposed method

Color Similarity

Object Texture Description

Object Localization and Model Update

Search Region Extraction in The Next Frame

• Experiment Result

• Conclusion

Object Localization and Model Update

• Object localization in the search region is performed by taking into account CH and LSK similarity

of patch to the 1ROI in the previous frame and the 2object instance in the stack.

• First, divide the search region into overlapping patches of size equal to the detected object. And

for each patch, we extract CH and LSK features.

• Then, for each patch, we construct three cosine similarity matrices

LSK similarity. Between this patch and the detected object in the previous frame.

LSK similarity. Between this patch and the last updated object instance.

CH similarity. Between this patch and the last updated object instance.

Object Localization and Model Update

• The new ROI is decided with the final decision matrix, which is computed by . (* denotes the

element-wise matrix multiplication and λ usually takes the value 0.5)

• The new candidate object position is at the patch with the maximal value maxi,j(Mij).

• We compare Mij with previous frame’s maximal value Mij’. If the values drops under a threshold, it

indicates a possible change in the object appearance.

• Other 4 decision matrix of rotation and scaling are calculated. The final decision for the new object

is the one which corresponds to the maximal value of five decision matrices.

• The newly localized object is stored in a stack, which size is constant.

Overview

• Introduction

• Proposed method

Color Similarity

Object Texture Description

Object Localization and Model Update

Search Region Extraction in The Next Frame

• Experiment Result

• Conclusion

Search Region Extraction in The Next Frame

• The position of the object in the following frame is predicted using a linear Kalman filter.

• The object motion state , and the new state is given by . denotes the process noise, with

probability distribution.

• After get , we can then get to compute the equation :

• Where is a covariance matrix with stochastic model. And this model is adjusted through

equations below.

• Among the equation above, is the predicted position of a search region’s center.

Overview

• Introduction

• Proposed method

• Experiment Result

• Conclusion

Experiment Result

• Quantitative evaluation comparison is performed through the frame detection accuracy (FDA)

measure.

• FDA calculates the overlap area between the ground truth object and the detected object D at a

given frame t.

Experiment Result

• The performance of proposed tracker is compared with two other trackers, PF tracker and FT

tracker.

• Test case:

Experiment Result

Experiment Result

• Test Case1 : test for variation of object scale

Experiment Result

• Test Case2 : test for variation of object scale

Experiment Result

• Test Case3 : test for variation of object rotation

Experiment Result

• Test Case4 : test for variation of partial occlusion

Experiment Result

• Test Case5 : test for variation of partial occlusion; the man walks behind the woman

Experiment Result

• Test Case6 : test for strong change in illumination

Experiment Result

• Test Case7 : test for human activity (orientation of glass)

Experiment Result

• Test Case8 : test for human activity (hands are articulated objects)

Experiment Result

• Test Case9 : test for human activity

Experiment Result

• Test Case10 : test for face tracking

Experiment Result

Overview

• Introduction

• Proposed method

• Experiment Result

• Conclusion

Conclusion

• The tracker extracted a representation of the target object based on LSK and CH at frame and

tried to find its location in the frame .

• Proposed method is effective in object tracking under severe changes in appearance, affine

transformations, and partial occlusion.

• The method cannot handle the case of full occlusion. (The tracker continues tracking another

object in the background)

• Kalman filter cannot follow sudden changes in the object direction or speed. (Although a larger

search region may solve the issue, but it would result in rapid decrease of speed)