In this paper, a localized particle subset method is proposed to solve target tracking problem in a Bayesian inference framework. Instead of using all particles to estimated the posterior probability density function (pdf) of targets, a subset is used. This subset of particles is selected by estimated motion of the targets. The weights of particles are updated by the 3D Hausdroff distances between target appearance model and samples. The proposed method is highly efficient in utilizing the particles, which consequently results in reduction of samples utilized in the prediction and update processes. It is also able to alleviate the sample degeneracy and impoverishment problems in the sampling process. Experiments show that the computation complexity for localized particle subset tracker is reduce to a fraction of that of the Sequential Importance (SIS) tracker but with compatible performance.
This paper proposes a global motion model estimation and target detection algorithm for surveillance and tracking applications. The proposed algorithm analyzes the foreground-background structure of a video frame, and detects objects with independent motions. Each video frame is first segmented into regions where image intensity and motion fields are homogeneous. Then global motion model fitting is accomplished using linear regression of motion vectors through iterations of region search. With the use of non-parametric estimation of motion field, the proposed methods is more efficient than direct estimation of motion parameter; and it is able to detect outliers where independent moving targets are located. The proposed algorithm is more computationally efficient than parametric motion estimation, and also more robust than a variety of background compensation based detection.
This paper describes an automatic video target tracking system that operates on the panoramic image provided by an image mosaicking preprocessing stage. In the mosaic preprocessing stage, a feature-based algorithm is applied to obtain the underlying homography between consecutive frames in a video sequence. With the first frame in the sequence chosen as the base image plane, subsequent frames are warped and merged into a panoramic scene for the video tracking stage. The tracking algorithm calculates the motion vector for each block in a warped frame by comparing it with the panoramic image, and those exceeding the dominant background motion can be considered as blocks belonging to potential moving foreground objects. Image segmentation is then used to recover the boundaries of the foreground objects. After fusing the labeled boundaries with the motion vector information, the potential targets, as well as their feature vectors, are identified. The feature vectors include information pertaining to location, size, and optical characteristics, and are input into a sub-tracker for record keeping. The input to the proposed system is a video stream from a single camera.
In this paper, we present an intelligent image compression system whereby regions of interest (ROI) and background information are coded independently of each other. We apply less compression (more bits) to regions of interest (targets), and more compression (fewer bits) to background data. This methodology preserves relevant features of targets for further analysis, and preserves the background only to the extent of providing contextual information. The resulting system dramatically reduces the bandwidth/storage requirements of the digital imagery, while preserving the target-specific utility of the imagery.
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