Kernel-based high-dimensional histogram estimation for visual tracking

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Presented at the 15th IEEE International Conference on Image Processing, October 12–15, 2008, San Diego, California, U.S.A.DOI: 10.1109/ICIP.2008.4711862We propose an approach for non-rigid tracking that represents objects by their set of distribution parameters. Compared to joint histogram representations, a set of parameters such as mixed moments provides a significantly reduced size representation. The discriminating power is comparable to that of the corresponding full high dimensional histogram yet at far less spatial and computational complexity. The proposed method is robust in the presence of noise and illumination changes, and provides a natural extension to the use of mixture models. Experiments demonstrate that the proposed method outperforms both full color mean-shift and global covariance searches

An improved spatiogram similarity measure for robust object localisation

Spatiograms were introduced as a generalisation of the commonly used histogram, providing the flexibility of adding spatial context information to the feature distribution information of a histogram. The originally proposed spatiogram comparison measure has significant disadvantages that we detail here. We propose an improved measure based on deriving the Bhattacharyya coefficient for an infinite number of spatial-feature bins. Its advantages over the previous measure and over histogram-based matching are demonstrated in object tracking scenarios

Thermo-visual feature fusion for object tracking using multiple spatiogram trackers

In this paper, we propose a framework that can efficiently combine features for robust tracking based on fusing the outputs of multiple spatiogram trackers. This is achieved without the exponential increase in storage and processing that other multimodal tracking approaches suffer from. The framework allows the features to be split arbitrarily between the trackers, as well as providing the flexibility to add, remove or dynamically weight features. We derive a mean-shift type algorithm for the framework that allows efficient object tracking with very low computational overhead. We especially target the fusion of thermal infrared and visible spectrum features as the most useful features for automated surveillance applications. Results are shown on multimodal video sequences clearly illustrating the benefits of combining multiple features using our framework

Real-time On-board Object Tracking for Cooperative Flight Control

One of possible cooperative Situations for flights could be a scenario when the decision on a new path is taken by A Certain fleet member, who is called the leader. The update on the new path is Transmitted to the fleet members via communication That can be noisy. An optical sensor can be used as a back-up for re-estimating the path parameters based on visual information. For A Certain topology, the issue can be solved by continuous tracking of the leader of the fleet in the video sequence and re-adjusting parameters of the flight, accordingly. To solve such a problem of a real time system has been developed for Recognizing and tracking 3D objects. Any change in the 3D position of the leading object is Determined by the on-board system and adjustments of the speed, pitch, yaw and roll angles are made to sustain the topology. Given a 2D image acquired by an on-board camera, the system has to perform the background subtraction, recognize the object, track it and evaluate the relative rotation, scale and translation of the object. In this paper, a comparative study of different algorithms is Carried out based on time and accuracy constraints. The solution for 3D pose estimation is provided based on the system of invariant Zernike moments. The candidate techniques solving the complete set of procedures have been Implemented on Texas Instruments TMS320DM642 EVM board. It is shown That 14 frames per second can be processed; That supports the real time Implementation of the tracking system with the reasonable accuracy

A combination of particle filtering and deterministic approaches for multiple kernel tracking.

International audienceColor-based tracking methods have proved to be efficient for their robustness qualities. The drawback of such global representation of an object is the lack of information on its spatial configuration, making difficult the tracking of more complex motions. This issue is overcome by using several kernels weighting pixels locations. In this paper a multiple kernels configuration is proposed and developed in both probabilistic and deterministic frameworks. The advantages of both approaches are combined to design a robust tracker allowing to track location, size and orientation of the object. A visual servoing application in tracking a moving object validates the proposed approach

Fragment based tracking for scale and orientation adaptation

In this work, we propose a simple yet highly effective algorithm for tracking a target through significant scale and orientation change. We divide the target into a number of fragments and tracking of the whole target is achieved by coordinated tracking of the individual fragments. We use the mean shift algorithm to move the individual fragments to the nearest minima, though any other method like integral histograms could also be used. In contrast to the other fragment based approaches, which fix the relative positions of fragments within the target, we permit the fragments to move freely within certain bounds. Furthermore, we use a constant velocity Kalman filter for two purposes. Firstly, Kalman filter achieves robust tracking because of usage of a motion model. Secondly, to maintain coherence amongst the fragments, we use a coupled state transition model for the Kalman filter. Using the proposed tracking algorithm, we have experimented on several videos consisting of several hundred frames length each and obtained excellent results

Scale-adaptive spatial appearance feature density approximation for object tracking

Object tracking is an essential task in visual traffic surveillance. Ideally, a tracker should be able to accurately capture an object's natural motion such as translation, rotation, and scaling. However, it is well known that object appearance varies due to changes in viewing angle, scale, and illumination. They introduce ambiguity to the image cue on which a visual tracker usually relies and which affects the tracking performance. Thus, a robust image appearance cue is required. This paper proposes scale-adaptive spatial appearance feature density approximation to represent objects and construct the image cue. It is found that the appearance representation improves the sensitivity on both the object's rotation and scale. The image cue is then constructed by both the appearance representation of the object and its surrounding background such that distinguishable parts of an object can be tracked under poor imaging conditions. Moreover, tracking dynamics is integrated with the image cue so that objects are efficiently localized in a gradient-based process. Comparative experiments show that the proposed method is effective in capturing the natural motion of objects and generating better tracking accuracy under different image conditions. © 2010 IEEE.published_or_final_versio

Object tracking and detection after occlusion via numerical hybrid local and global mode-seeking

Given an object model and a black-box measure of similarity between the model and candidate targets, we consider visual object tracking as a numerical optimization problem. During normal tracking conditions when the object is visible from frame to frame, local optimization is used to track the local mode of the similarity measure in a parameter space of translation, rotation and scale. However, when the object becomes partially or totally occluded, such local tracking is prone to failure, especially when common prediction techniques like the Kalman filter do not provide a good estimate of object parameters in future frames. To recover from these inevitable tracking failures, we consider object detection as a global optimization problem and solve it via Adaptive Simulated Annealing (ASA), a method that avoids becoming trapped at local modes and is much faster than exhaustive search. As a Monte Carlo approach, ASA stochastically samples the parameter space, in contrast to local deterministic search. We apply cluster analysis on the sampled parameter space to redetect the object and renew the local tracker. Our numerical hybrid local and global mode-seeking tracker is validated on challenging airborne videos with heavy occlusion and large camera motions. Our approach outperforms state-of-the-art trackers on the VIVID benchmark datasets. 1

Fast global kernel density mode seeking with application to localisation and tracking

Copyright © 2005 IEEE.We address the problem of seeking the global mode of a density function using the mean shift algorithm. Mean shift, like other gradient ascent optimisation methods, is susceptible to local maxima, and hence often fails to find the desired global maximum. In this work, we propose a multi-bandwidth mean shift procedure that alleviates this problem, which we term annealed mean shift, as it shares similarities with the annealed importance sampling procedure. The bandwidth of the algorithm plays the same role as the temperature in annealing. We observe that the over-smoothed density function with a sufficiently large bandwidth is uni-modal. Using a continuation principle, the influence of the global peak in the density function is introduced gradually. In this way the global maximum is more reliably located. Generally, the price of this annealing-like procedure is that more iterations are required. Since it is imperative that the computation complexity is minimal in real-time applications such as visual tracking. We propose an accelerated version of the mean shift algorithm. Compared with the conventional mean shift algorithm, the accelerated mean shift can significantly decrease the number of iterations required for convergence. The proposed algorithm is applied to the problems of visual tracking and object localisation. We empirically show on various data sets that the proposed algorithm can reliably find the true object location when the starting position of mean shift is far away from the global maximum, in contrast with the conventional mean shift algorithm that will usually get trapped in a spurious local maximum.Chunhua Shen, Michael J. Brooks and Anton van den Henge