Objective

2

Visual object tracking is a significant computer vision task that can be applied to many domains

such as military

robotics

intelligent visual surveillance

human-computer interaction

and medical diagnosis. A large variety of trackers that have been proposed in the literature in the past decades have delivered satisfactory performances. Despite the success of researching on this topic

visual object tracking still suffers from difficulties in handling complex object appearance changes caused by factors such as illumination

partial occlusion

shape deformation

background clutter

low contrast

specularities

camera motion

and at least seven more aspects. Generally

visual tracking is a search (or classification) problem that continuously infers the state of a target in video sequences

aims to identify the candidate while it matches to the target template accurately

and returns it as a tracking result. Constructing an effective and high-performance tracker has two core issues. The first is the issue of representative feature learning and high-level modeling. The second is the problem of filtering and efficient searching. Given that the target states in every video frame are represented using several online learned feature templates

the modeling capability of the tracker will significantly depend on the generalizability of template data and accurate model representation with error estimation precision because of the complex interference factors caused by the target itself or the scene conditions. In addition

the relationship between each data pixel is significantly damaged while its original data structures are being changed because the sample data are intentionally forced into vector form in most existing algorithms. Moreover

the computational complexity with high data dimensionality must be increased. Therefore

designing an effective model representation mechanism of the 2D appearance of moving objects with the appropriate data expression is the key issue for the success of a visual tracker.

Method

2

In this study

the appearance model representation problem of generative-model-based visual object tracking algorithm is investigated in depth. In a prior work

we formulated the observation model via tensor (3D array) nuclear norm regularization. The tracker is called tensor nuclear norm regression-based tracker (TNRT) and has achieved favorable results in many tracking environments. However

the TNRT requires high hardware conditions and graphics processing unit computing demands

which will lead to slow tracking speeds if some practical uses require low hardware conditions. Therefore

we redesign a novel matrix low-rank representation-based observation model and its corresponding likelihood measurement function

as well as maintain several good properties of the TNRT algorithm

such as multitask joint learning

nuclear norm regularization-based model representation

and original data structures of sample signals. In the proposed tracking framework

several critical feature templates (dictionary or subspace) are learned from online data using the incremental principal component analysis algorithm. Then

in accordance with the appearance information of an incoming video frame

the proposed appearance modeling mechanism will use the feature templates to represent the target candidate linearly with independent and identically distributed Gaussian-Laplacian mixture noise by adopting the multitask joint learning strategy. Subsequently

the matrix nuclear norm and weighted

$$ {\rm L}_1$$

-norm-based joint maximum likelihood function measure the distances between target candidates and feature subspace scrupulously. Given that the intrinsic data structures of samples are guaranteed using the matrix form and the spatial distributions of visual features remain intact

the proposed multitask observation modeling via matrix low-rank regularization-based objective function will construct more accurate and flexible sample signals than

$$ {\rm L}_1$$

$$ {\rm L}_2$$

or other hybrid regularization-based model representation methods. Then

in every frame

the identical likelihood measurement function of our algorithm measures each candidate sample with obvious comparability. Finally

the tracker is able to explore the potential characteristics of the sample data fully and further detect the complex appearance changes of the target with some challenging disturbances

such as occlusion or strong illuminations. Meanwhile

the observation model

which formulates matrix-form-based data prototypes

can improve the tracking speed remarkably with its distinctly reduced data dimensionality and low computational complexity.

Result

2

Although the pixels of residual data always show similar grayscale intensities and share some spatial information with 2D data prototypes

such as block-shaped linking areas

the conventional observation model using

$$ {\rm L}_1$$

$$ {\rm L}_2$$

or other hybrid regularization-based model representation methods cannot fully examine the potential structure of residual data. In comparison to these traditional methods

the matrix low-rank regression model (MLRM) more precisely explores the residual data and further detects the spatial characteristics of reconstruction error. In other words

the MLRM significantly discovers the low-rank characteristics of the residual matrix. In this study

we aim to evaluate our proposed tracking algorithm systematically and experimentally on 10 public video fragments that cover the previously mentioned challenging noisy factors and compare it with several state-of-the-art algorithms commonly cited in influential literature. We indicate that each tracker can be evaluated objectively using survival curves

such as average center point error (ACE)

average overlap rate (AOR)

and average success rate (ASR). Our tracking algorithm reflects the favorable robustness in these noisy environments and obtains the best results in each video sequence

with ACE

AOR

and ASR of 5.29 pixels

78%

and 98.28%

respectively.

Conclusion

2

In this study

a novel multitask matrix low-rank model representation method and its corresponding maximum likelihood estimation function are designed. The analysis of a large variety of circumstances in several public video sequences provides objective insight into the strengths and weaknesses of each tracker. The appearance modeling mechanism and maximum likelihood estimation function of the proposed MLRM algorithm play critical roles and achieve favorable tracking results in several challenging video sequences. Qualitative and quantitative experimental evaluations of a number of challenging noisy environments indicate that the proposed MLRM algorithm can reflect the best robustness to elevate the model degradation or drifting problem caused by occlusion and strong illumination and can achieve the same or even better results when compared with several state-of-the-art algorithms.