Neuro-probabilistic model for object tracking

Pattern Analysis and Applications - Occlusion is one of the major challenges for object tracking in real-life scenario. Various techniques in particle filter framework have been developed to solve...     

Pearson-based mixture model for color object tracking

To track objects in video sequences, many studies have been done to characterize the target with respect to its color distribution. Most often, the Gaussian mixture model (GMM) is used to represent the object color density. In this paper, we propose to extend the normality assumption to more general families of distributions issued from the Pearson’s system. Precisely, we propose a method called Pearson mixture model (PMM), used in conjunction with Gaussian copula, which is dynamically updated to adapt itself to the appearance change of the object during the sequence. This model is combined with Kalman filtering to predict the position of the object in the next frame. Experimental results on gray-level and color video sequences show tracking improvements compared to classical GMM. Especially, the PMM seems robust to illumination variations, pose and scale changes, and also to partial occlusions, but its computing time is higher than the computing time of GMM.     

A probabilistic object tracking model based on condensation algorithm

Object tracking, which has many application in our daily life, is an important topic in electronics engineering area. It basically deals with estimation and location of an object in given video frames. In this paper, a novel object tracking algorithm based on particle filtering associate with population balances is proposed. The developed algorithm was used to track objects in synthetic frames and natural video frames. According to results, it has high accuracy level for single and multi-object tracking.     

A multiple feature fused model for visual object tracking via correlation filters

Multimedia Tools and Applications - Common tracking algorithms only use a single feature to describe the target appearance, which makes the appearance model easily disturbed by noise. Furthermore,...     

Multi-scale patch-based sparse appearance model for robust object tracking

When objects undergo large pose change, illumination variation or partial occlusion, most existing visual tracking algorithms tend to drift away from targets and even fail to track them. To address the issue, in this paper we propose a multi-scale patch-based appearance model with sparse representation and provide an efficient scheme involving the collaboration between multi-scale patches encoded by sparse coefficients. The key idea of our method is to model the appearance of an object by different scale patches, which are represented by sparse coefficients with different scale dictionaries. The model exploits both partial and spatial information of targets based on multi-scale patches. Afterwards, a similarity score of one candidate target is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Additionally, to decrease the visual drift caused by frequently updating model, we present a novel two-step object tracking method which exploits both the ground truth information of the target labeled in the first frame and the target obtained online with the multi-scale patch information. Experiments on some publicly available benchmarks of video sequences showed that the similarity involving complementary information can locate targets more accurately and the proposed tracker is more robust and effective than others.     

A graphical model based solution to the facial feature point tracking problem

In this paper a facial feature point tracker that is motivated by applications such as human-computer interfaces and facial expression analysis systems is proposed. The proposed tracker is based on a graphical model framework. The facial features are tracked through video streams by incorporating statistical relations in time as well as spatial relations between feature points. By exploiting the spatial relationships between feature points, the proposed method provides robustness in real-world conditions such as arbitrary head movements and occlusions. A Gabor feature-based occlusion detector is developed and used to handle occlusions. The performance of the proposed tracker has been evaluated on real video data under various conditions including occluded facial gestures and head movements. It is also compared to two popular methods, one based on Kalman filtering exploiting temporal relations, and the other based on active appearance models (AAM). Improvements provided by the proposed approach are demonstrated through both visual displays and quantitative analysis.     

A graphical model for evolutionary optimization

We present a statistical model of empirical optimization that admits the creation of algorithms with explicit and intuitively defined desiderata. Because No Free Lunch theorems dictate that no optimization algorithm can be considered more efficient than any other when considering all possible functions, the desired function class plays a prominent role in the model. In particular, this provides a direct way to answer the traditionally difficult question of what algorithm is best matched to a particular class of functions. Among the benefits of the model are the ability to specify the function class in a straightforward manner, a natural way to specify noisy or dynamic functions, and a new source of insight into No Free Lunch theorems for optimization.     

A novel algebraic method for kernel-based object tracking

In the present paper, a new tracking method based on kernel tracking is proposed. The proposed method employs a novel algebraic algorithm to get the kernel movement. In contrast to the mean-shift method which uses a weighted kernel to reduce the effect of the background, the algebraic algorithm of the proposed method allows dividing the candidate area into two parts in order to identify the object and background regions. To detect the object and background regions, we propose measuring the similarity of weighted histogram for each part. The experiments show the superiority of the proposed method for the removal of the background. The effect of noise and background clutter is reduced by segmentation of the object which produces the narrow histogram. In conclusion, the ability of the proposed method for tracking in crowded and cluttered scenes is demonstrated.     

An oscillatory neural model of multiple object tracking

An oscillatory neural network model of multiple object tracking is described. The model works with a set of identical visual objects moving around the screen. At the initial stage, the model selects into the focus of attention a subset of objects initially marked as targets. Other objects are used as distractors. The model aims to preserve the initial separation between targets and distractors while objects are moving. This is achieved by a proper interplay of synchronizing and desynchronizing interactions in a multilayer network, where each layer is responsible for tracking a single target. The results of the model simulation are presented and compared with experimental data. In agreement with experimental evidence, simulations with a larger number of targets have shown higher error rates. Also, the functioning of the model in the case of temporarily overlapping objects is presented.     

An active contour model for object tracking using the previous contour

An active contour model is proposed for object tracking using prior information. Conventional algorithms have many problems when applied in object tracking. The proposed active contour algorithm, a model using an edge of an adapted color feature, not only modifies the internal energy function of the conventional algorithm to extend the search range and reduce the computational burden, but also modifies the external energy function to reduce the edge candidates of the object. The algorithm searches normally and uses dynamic programming to solve the energy minimization problem. The main drawbacks of a conventional snake algorithm, i.e., shrinking, a limited search range, sensitivity to outliers, are improved with the proposed algorithm. We illustrate the effectiveness of our scheme using some tracking examples. This work was presented, in part, at the Seventh International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

A distributed approach for real-time multi-camera multiple object tracking

Estimating the positions of a set of moving objects captured from a network of cameras is still an open problem in Computer Vision. In this paper, a distributed and real-time approach for tracking multiple objects on multiple cameras is presented. A quantitative comparison with six state-of-the-art methods has been carried out on the publicly available PETS 2009 data set, demonstrating the effectiveness of the algorithm. Moreover, the proposed method has been tested also on a multi-camera soccer data set, showing its data fusion capabilities.     

Robust object tracking using a sparse coadjutant observation model

Multimedia Tools and Applications - This paper develops a classical visual tracker that is called a discriminative sparse similarity (DSS) tracker. Based on the classical Laplacian multi-task...     

Across-camera object tracking using a conditional random field model

The Journal of Supercomputing - To ensure safety, most public spaces now deploy monitoring systems. However, in most scenarios, the tracking operations of these monitoring systems are performed...     

Kernel-based object tracking

A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking.     

Collaborative strategy for visual object tracking

Multimedia Tools and Applications - Adaptively learning the difference between object and background, discriminative trackers are able to overcome the complex background problem in visual object...     

A hybrid scheme for energy-efficient object tracking in sensor networks

Energy saving is a critical issue in many sensor-network-based applications. Among the existing sensor-network-based applications, the surveillance application has attracted extensive attention. Object tracking in sensor networks (OTSNs) is a typical surveillance application. Previous studies on energy saving for OTSNs can be divided into two main approaches: (1) improvements in hardware design to lower the energy consumption of attached components and (2) improvements in software to predict the movement of objects. In this paper, we propose a novel scheme, namely hybrid tracking scheme (HTS), for tracking objects with energy efficiency. The scheme consists of the two parts: (1) adaptive schedule monitoring and (2) a recovery mechanism integrated with seamless temporal movement patterns and seeding-based flooding to relocate missing objects with the purpose of saving energy. Furthermore, we also propose a frequently visited periods mining algorithm, which discovers the corresponding frequently visited periods for adaptive schedule monitoring efficiently from the visitation information of sensor nodes. To decrease the number of sensor nodes activated in flooding, a seeding-based flooding mechanism is first proposed in our work. Empirical evaluations of various simulation conditions and real datasets show that the proposed HTS delivers excellent performance in terms of energy efficiency and low missing rates.     

A database model for object dynamics

To effectively model complex applications in which constantly changing situations can be represented, a database system must be able to support the runtime specification of structural and behavioral nuances for objects on an individual or group basis. This paper introduces the role mechanism as an extension of object-oriented databases to support unanticipated behavioral oscillations for objects that may attain many types and share a single object identity. A role refers to the ability to represent object dynamics by seamlessly integrating idiosyncratic behavior, possibly in response to external events, with pre-existing object behavior specified at instance creation time. In this manner, the same object can simultaneously be an instance of different classes which symbolize the different roles that this object assumes. The role concept and its underlying linguistic scheme simplify the design requirements of complex applications that need to create and manipulate dynamic objects. Edited by D. McLeod / Received March 1994 / Accepted January 1996     

A database model for object dynamics

Due to a technical error, some figures of the above paper were not reproduced satisfactorily. They are printed again below: Edited by D. McLeod / Received March 1994 / Accepted January 1996