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Proceedings ArticleDOI

Online Improved Eigen Tracking

04 Feb 2009-pp 278-281
TL;DR: A novel predictive statistical framework is presented to improve the performance of an Eigen Tracker which uses fast and efficient eigen space updates to learn new views of the object being tracked on the fly using candid co-variance free incremental PCA.

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Abstract: We present a novel predictive statistical framework to improve the performance of an Eigen Tracker which uses fast and efficient eigen space updates to learn new views of the object being tracked on the fly using candid co-variance free incremental PCA. The proposed system detects and tracks an object in the scene by learning the appearance model of the object online motivated by non-traditional uniform norm. It speeds up the tracker many fold by avoiding nonlinear optimization generally used in the literature.

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Summary (2 min read)

Introduction

  • There are numerous tracking algorithms proposed in the literature like mean-shift or camshift algorithms, appearance based tracker etc.
  • An appearance-based tracker (EigenTracker [1]) can track moving objects undergoing appearance changes powered by dimensionality reduction techniques.
  • The Isard and Blake CONDENSATION algorithm [2] can represent simultaneous multiple hypothesis.
  • There can be several ways by virtue of which the power of EigenTracker and particle filter can be combined like [7] and [8].
  • The main features of their approach are the tracker initialization, presence of prediction framework, effective subspace update algorithm [4] and avoidance of non-linear optimizations.

2.1. The Prediction Mechanism

  • These 5 motion parameters can track the object with its bounding box being an oriented rectangle.
  • This seed point is needed for sampling windows around it.
  • The predictive framework helps generating better seed values for diverse object dynamics.
  • The measurement is the set of five motion parameters obtained from the image, Zt. The observation model has Gaussian peaks around each observation, and constant density otherwise.
  • The state estimate is used to generate the predictions for the next frame.

2.2. Initialization of the tracker

  • Accurate tracker initialization is a difficult problem.
  • The authors have used a moving object segmentation method based on the improved PCA which is a simplified version of the methodology used in [3] for moving object detection and segmentation.
  • For this technique to work the background should be still or changing slowly such as grassplot or cloud for the analyzing frames.
  • Secondly, the calculation result is improved in the following way.
  • E effectively eliminates the blur of the eigen images of the moving object.

2.3. On-the-fly Eigen space Updates

  • In most tracking problems, the object of interest undergoes changes in appearance over time.
  • It is not feasible to learn all possible poses and shapes even for a particular domain of application, off-line.
  • Therefore, one needs to learn and update the relevant Eigen spaces on the fly.
  • Since a naive O(mN3) algorithm (for N images having m pixels each) is time-consuming, the authors use an efficient-estimation motivated by optimal incremental principal component analysis of O(mNk) algorithm (for k most significant singular values) proposed by Juyang Weng et al. [4].
  • The data entering the IPCA algorithms are the scatter vectors, u(n) = x(n) – m’(n) for n=1,2,….

2.4. The Overall Tracking Scheme

  • The following section outlines their overall tracking scheme.
  • For all subsequent frames, the next step is to obtain the measurements – taking the minimum distant prediction from the learnt sub-space (in RGB plane) as the description of the tracked object.
  • The authors then update the eigen-spaces incrementally.
  • Finally, the authors predict the motion parameters values for the next frame.
  • The idea behind the subspace construction for the appearance based tracking is the uniform L2 reconstruction error norm Error∞(L, {x1, · · · xN}) = maxid2(L, xi) (5) To define the quality of approximation, the authors use the uniform reconstruction error norm Error∞ introduced in Equation 5 in their approach.

4. Remark and Discussions

  • The computational complexity of the algorithm is dominated by the number of windows generated from the sampling.
  • Like all appearance-based tracker it cannot handle situation like sudden pose or illumination changes or fully occlusion, but it can handle partial occlusion and gradual pose or illumination changes .
  • There are three important free parameters in their algorithm, N, the number of samples to pick and l, amnesic parameter for the subspace update and k, the number of principal components.

6. Experiments and Results

  • The authors current implementation runs at about 0.25 to 0.5 frames/sec with 320x240 and 176x144 video input respectively on a standard Intel centrino P4 1.8 MHz machine and thus it is quite expected that C implementation easily can run on real time.
  • The authors test cases contain scenarios which a real-world tracker encounters, including changes in appearance, large pose variations, significant lighting variation and shadowing, partial occlusion, object partly leaving field of view, large scale changes, cluttered backgrounds, and quick motion resulting in motion blur.
  • It is evident from the above table that incorporation of predictive framework makes the tracker more robust.
  • Coastguard sequence has presence of the boat up to frames 100 out of total 300 frames and then it disappears .
  • Hall is the sequence where a person (tracking object) appears in frame 25 and disappears after 140th frame, and in that interval it changes poses heavily.

7. Summary and conclusions

  • The authors have introduced a technique for predictively learning the statistical distribution on-line with an Eigen subspace representation of an object that is being tracked with a fast EigenSpace update technique.
  • The resulting tracker is both simple and fast.
  • The method can robustly track an object in the presence of large viewpoint changes, partial occlusion, lighting variation, changes to the shape of the object shaky cameras, and motion blur.
  • Moreover avoidance of non-linear optimization makes their tracking task faster than that of [7].

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Figures (4)
  • Table 1: comparison of predictive and non-predictive framework ( N = 150 windows sampled for each case)
    Table 1: comparison of predictive and non-predictive framework ( N = 150 windows sampled for each case)
  • Figure 3: Sequence of tracking a woman’s face (sequence Renata) which shows apparent pose changes
    Figure 3: Sequence of tracking a woman’s face (sequence Renata) which shows apparent pose changes
  • Figure 2 Sequence of tracking a helicopter in a changing background and which goes under partial occlusion
    Figure 2 Sequence of tracking a helicopter in a changing background and which goes under partial occlusion
  • Figure 1: Sequence of tracking a boat (sequence coastguard) which shows high background motion, background clutter as well as object partly going out of the field of view
    Figure 1: Sequence of tracking a boat (sequence coastguard) which shows high background motion, background clutter as well as object partly going out of the field of view
Citations
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Patent
04 Sep 2009-
Abstract: A video compression framework based on parametric object and background compression is proposed. At the encoder, an embodiment detects objects and segments frames into regions corresponding to the foreground object and the background. The object and the background are individually encoded using separate parametric coding techniques. While the object is encoded using the projection of coefficients to the orthonormal basis of the learnt subspace (used for appearance based object tracking), the background is characterized using an auto-regressive (AR) process model. An advantage of the proposed schemes is that the decoder structure allows for simultaneous reconstruction of object and background, thus making it amenable to the new multi-thread/multi-processor architectures.

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27 citations


Journal ArticleDOI
31 May 2017-Journal of Imaging
TL;DR: The proposed, designed and implemented system robustly tracks the target object present in the scene in real time for standard PAL (720 × 576) resolution color video and automatically controls camera movement in the direction determined by the movement of the tracked object.

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Abstract: The design of smart video surveillance systems is an active research field among the computer vision community because of their ability to perform automatic scene analysis by selecting and tracking the objects of interest In this paper, we present the design and implementation of an FPGA-based standalone working prototype system for real-time tracking of an object of interest in live video streams for such systems In addition to real-time tracking of the object of interest, the implemented system is also capable of providing purposive automatic camera movement (pan-tilt) in the direction determined by movement of the tracked object The complete system, including camera interface, DDR2 external memory interface controller, designed object tracking VLSI architecture, camera movement controller and display interface, has been implemented on the Xilinx ML510 (Virtex-5 FX130T) FPGA Board Our proposed, designed and implemented system robustly tracks the target object present in the scene in real time for standard PAL (720 × 576) resolution color video and automatically controls camera movement in the direction determined by the movement of the tracked object

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8 citations


Cites methods from "Online Improved Eigen Tracking"

  • ...[40] have enhanced the capabilities of the Eigen tracker by augmenting it with a condensation-based predictive framework to increase its efficiency and also made it fast by avoiding non-linear optimizations....

    [...]


Proceedings ArticleDOI
10 Nov 2014-
TL;DR: A modified PN learning algorithm is proposed for enhancing the performance of object tracking systems by reducing the detector errors and achieving performance improvement to increase the frame processing by adding background subtraction technique for any real time object detection.

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Abstract: system presents an automatic long term tracking and learning and detection of real time objects in the live video stream. In this system, Object to be tracked also called as cropped image is defined by its location and the extent in the single frame by selecting the object of interest in the live video. Many existing systems for tracking objects fails due to loss of information caused by complex shapes, rapid motion, illumination changes, scaling and projection of 3D world on 2D image. Proposed modified PN learning algorithm which uses background subtraction technique to increase speed of the frame processing for object detection. Proposed Modified PN learning algorithm considers the object to be tracked as P- Type Object and background is divided into the numbers of N-Type objects. Initially input image is matched with the N- Type of objects for rejection and then with P-type for acceptance. Proposed system uses the Template Matching algorithm to match cropped image with region of interest in the current frame to mark the Object Location. If match is found then Principle Component Analysis algorithm is used for detection of the fast moving object which is the advantage over the existing systems. If match does not found then Proposed Modified PN learning processing is applied to detect the image in rapid motion video. Proposed system uses background subtraction to increase the performance for detection of any moving object as the background remains still and we get approximate location of the moving object. Proposed System is expected to minimize delay for frame processing and reduce average localization errors to improve in matching percentage irrespective of scaling of the input image. Thus proposed system is expected to overcome the drawbacks of existing system for efficient tracking of any real time object.

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5 citations


Patent
30 Dec 2011-
Abstract: A video compression framework based on parametric object and background compression is proposed. At the encoder, an object is detected and frames are segmented into regions corresponding to the foreground object and the background. The encoder generates object motion and appearance parameters. The motion or warping parameters may include at least two parameters for object translation; two parameters for object scaling in two primary axes and one object orientation parameter indicating a rotation of the object. Particle filtering may be employed to generate the object motion parameters. The proposed methodology is the formalization of the concept and usability for perceptual quality scalability layer for Region(s) of Interest. A coded video sequence format is proposed which aims at “network friendly” video representation supporting appearance and generalized motion of object(s).

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2 citations


Proceedings ArticleDOI
Ayesha Chaudhary1, Santanu Chaudhury1Institutions (1)
02 Dec 2013-
TL;DR: An architecture of a smart space built with robots and distributed smart cameras connected via a network using vision as the basic sensing mechanism and a MAP based object identification scheme which works on Grassmannian manifold is presented.

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Abstract: This paper presents an architecture of a smart space built with robots and distributed smart cameras connected via a network. We present a framework for tracking and recording presence of objects over a large space using vision as the basic sensing mechanism. A MAP based object identification scheme which works on Grassmannian manifold has been presented. Experimental results establish validity of the approach.

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1 citations


Cites background or methods from "Online Improved Eigen Tracking"

  • ...We use the algorithm given by authors in [14] et al for online appearance based tracking of the objects....

    [...]

  • ...The camera carries out appearance based tracking [14] on each of the objects in its view....

    [...]


References
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Journal ArticleDOI
Michael Isard1, Andrew Blake1Institutions (1)
TL;DR: The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set.

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Abstract: The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimo dal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.

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5,749 citations


"Online Improved Eigen Tracking" refers methods in this paper

  • ...The Isard and Blake CONDENSATION algorithm [2] can represent simultaneous multiple hypothesis....

    [...]


Journal ArticleDOI
Michael J. Black1, Allan D. Jepson2Institutions (2)
TL;DR: A “subspace constancy assumption” is defined that allows techniques for parameterized optical flow estimation to simultaneously solve for the view of an object and the affine transformation between the eigenspace and the image.

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Abstract: This paper describes an approach for tracking rigid and articulated objects using a view-based representation. The approach builds on and extends work on eigenspace representations, robust estimation techniques, and parameterized optical flow estimation. First, we note that the least-squares image reconstruction of standard eigenspace techniques has a number of problems and we reformulate the reconstruction problem as one of robust estimation. Second we define a “subspace constancy assumption” that allows us to exploit techniques for parameterized optical flow estimation to simultaneously solve for the view of an object and the affine transformation between the eigenspace and the image. To account for large affine transformations between the eigenspace and the image we define a multi-scale eigenspace representation and a coarse-to-fine matching strategy. Finally, we use these techniques to track objects over long image sequences in which the objects simultaneously undergo both affine image motions and changes of view. In particular we use this “EigenTracking” technique to track and recognize the gestures of a moving hand.

...read more

1,337 citations


Journal ArticleDOI
Juyang Weng1, Yilu Zhang1, Wey-Shiuan Hwang1Institutions (1)
TL;DR: A fast incremental principal component analysis (IPCA) algorithm, called candid covariance-free IPCA (CCIPCA), used to compute the principal components of a sequence of samples incrementally without estimating the covariance matrix (so covariances-free).

...read more

Abstract: Appearance-based image analysis techniques require fast computation of principal components of high-dimensional image vectors. We introduce a fast incremental principal component analysis (IPCA) algorithm, called candid covariance-free IPCA (CCIPCA), used to compute the principal components of a sequence of samples incrementally without estimating the covariance matrix (so covariance-free). The new method is motivated by the concept of statistical efficiency (the estimate has the smallest variance given the observed data). To do this, it keeps the scale of observations and computes the mean of observations incrementally, which is an efficient estimate for some well known distributions (e.g., Gaussian), although the highest possible efficiency is not guaranteed in our case because of unknown sample distribution. The method is for real-time applications and, thus, it does not allow iterations. It converges very fast for high-dimensional image vectors. Some links between IPCA and the development of the cerebral cortex are also discussed.

...read more

459 citations


8


"Online Improved Eigen Tracking" refers methods in this paper

  • ...The main features of our approach are the tracker initialization, presence of prediction framework, effective subspace update algorithm [4] and avoidance of non-linear optimizations....

    [...]


Proceedings ArticleDOI
19 Jul 2004-
TL;DR: This paper presents a simple but robust visual tracking algorithm based on representing the appearances of objects using affine warps of learned linear subspaces of the image space, and argues that a variant of it, the uniform L/sup 2/-reconstruction error norm, is the right one for tracking.

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Abstract: This paper presents a simple but robust visual tracking algorithm based on representing the appearances of objects using affine warps of learned linear subspaces of the image space. The tracker adaptively updates this subspace while tracking by finding a linear subspace that best approximates the observations made in the previous frames. Instead of the traditional L/sup 2/-reconstruction error norm which leads to subspace estimation using PCA or SVD, we argue that a variant of it, the uniform L/sup 2/-reconstruction error norm, is the right one for tracking. Under this framework we provide a simple and a computationally inexpensive algorithm for finding a subspace whose uniform L/sup 2/-reconstruction error norm for a given collection of data samples is below some threshold, and a simple tracking algorithm is an immediate consequence. We show experimental results on a variety of image sequences of people and man-made objects moving under challenging imaging conditions, which include drastic illumination variation, partial occlusion and extreme pose variation.

...read more

179 citations


6


"Online Improved Eigen Tracking" refers background or methods in this paper

  • ...[6] proposes a fast appearance tracker which eliminates non-linear optimizations completely but it lacks the benefit of predictive framework....

    [...]

  • ...We enhance the capabilities of the EigenTracker by augmenting it with a CONDENSATION-based predictive framework to increase its efficiency and also make it fast by avoiding non-linear optimization like [6]....

    [...]


Journal ArticleDOI
01 Feb 2007-
TL;DR: A novel eigenspace-based framework to model a dynamic hand gesture that incorporates both hand shape as well as trajectory information is presented and encouraging experimental results are shown on a such a representative set.

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Abstract: We present a novel eigenspace-based framework to model a dynamic hand gesture that incorporates both hand shape as well as trajectory information. We address the problem of choosing a gesture set that models an upper bound on gesture recognition efficiency. We show encouraging experimental results on a such a representative set.

...read more

48 citations


"Online Improved Eigen Tracking" refers background in this paper

  • ...There can be several ways by virtue of which the power of EigenTracker and particle filter can be combined like [7] and [8]....

    [...]


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