Tracking non-rigid objects such as people in video sequences is a daunting task due to computational complexity and unpredictable environment. The analysis and interpretation of video sequence containing moving, deformable objects have been an active research areas including video tracking, computer vision, and pattern recognition. In this paper we propose a robust, model-based, real-time system to cope with background clutter and occlusion. The proposed algorithm consists of following four steps: (i) localization of an object-of-interest by analyzing four directional motions, (ii) region tracker for tracking moving region detected by the motion detector, (iii) update of training sets using the Smart Snake Algorithm (SSA) without preprocessing, (iv) active shape model-based tracking in region information. The major contribution this work lies in the integration for a completed system, which covers from image processing to tracking algorithms. The approach of combining multiple algorithms succeeds in overcoming fundamental limitations of tracking and at the same time realizes real time implementation. Experimental results show that the proposed algorithm can track people under various environment in real-time. The proposed system has potential uses in the area of surveillance, sape analysis, and model-based coding, to name of few.
Monitoring of large sites requires coordination of multiple cameras, and methods for relating events between distributed cameras. This paper presents a planar trajectory estimation method using multiple cameras, and the use of estimated trajectories to facilitate camera calibration. The algorithm addresses the problem of recovering the relative pose of several stationary cameras that observe one or more objects in motion. Each camera tracks several objects to produce a set of trajectories in the image. Using a simple calibration procedure, we recover the relative orientation of each camera to the local ground plane. We also present experimental results on both indoor and outdoor sequences containing persons and vehicles.
Tracking non-rigid objects such as people in video sequences is a daunting task due to computational complexity and unstable performance. Special considerations for digital image processing are required when an object of interest changes its shape between consecutive frames. Traditionally active shape models (ASMs) have not include a color information in their formation. We present several extensions of the ASM for color images using different color-adapted objective functions. We also analyze the performance of color ASM models in RGB, YUV, or HIS color spaces.
This paper proposes a real-time digital auto-focusing algorithm using a priori estimated set of point spread functions
(PSFs). A priori set of PSFs are estimated by establishing the relation between two-dimensional PSF and onedimensional
step response whose elements are samples of profile of degraded step edge. From the priori estimated set,
the proposed auto-focusing algorithm can select the optimal PSF by the focusing criterion based on the frequency
domain analysis. We then use the constrained least square (CLS) filter to obtain the in-focused image with the estimated
optimal PSF. The proposed algorithm can be implemented in real-time because the set of PSFs are already estimated and
the filtering is performed in the frequency domain.
Video tracking systems are inherently provided with non-rigid objects with various shapes and sizes, which often result
in poor match of an initial model with the actual input shape. The hierarchical approach to the active shape
model(ASM) is essential for video tracking systems to deal with such unstable inputs. The contribution of this paper is
to improve the performance of active shape model-based real-time object tracking. We also propose a hierarchical
processing framework to reduce the computational overhead. In this paper, we present a Kalman filter-based
hierarchical ASM in order to estimate a dynamic shape in video object tracking. The experimental results show that the
proposed hierarchical active shape model using Kalman filter is efficient. The experimental results also show good
results when objects are partially occluded by other objects.
This paper proposes an adaptive regularized noise smoothing algorithm for range images using the area decreasing flow method, which can preserve meaningful edges during the smoothing process. Adaptation is incorporated by adjusting the regularization parameter according to the results of surface curvature analysis. In general, range data includes mixed noise such as Gaussian or impulsive noise. Although non-adaptive version of regularized noise smoothing algorithm can easily reduce Gaussian noise, impulsive noise caused by random fluctuation of the sensor acquisition is not easy to be removed from observed range data. It is also difficult to remove noise near edge using the existing adaptive regularization algorithms. In order to cope with the problem, the second smoothness constraint is additionally incorporated into the existing regularization algorithm, which minimizes the difference between the median filtered data and the estimated data. As a result, the proposed algorithm can effectively remove the noise of dense range data while meaningful edge is well-preserved.
Multiple camera based surveillance systems provide us with a more robust tracking of objects. To take advantage of additional cameras, it is necessary to establish geometrical relationship between the cameras and relationship between an object and a camera. In recent years several techniques have been proposed, which estimate only the
relation of a dominant ground plane between different views instead of fully geometrical relation of camera-object-camera. They are however neither fully automatic nor suitable for a non-planar ground. We propose a fully automatic calibration algorithm which can cope with complex environment, including non-planar ground. The proposed algorithm automatically tracks and matches objects between different views, determines the overlapped region, and aligns each piece-wisely segmented plane between two views. The proposed calibration
algorithm minimizes the effect of occlusion and improves the accuracy of 3D measurements by using multiple views. The algorithm can also provide us large field of view by concatenating a series of cameras.
This paper proposes a fully digital auto-focusing algorithm for restoring the image with differently out-of-focused objects, which can restore background as well as all objects. In this paper, we assume that out-of-focus blur is isotropic such as circle of confusion (COC) or two-dimensional Gaussian blur. Therefore, the proposed algorithm can segment and estimate the point spread function (PSF) by using the size of ramp in the one-dimensional step response. The proposed algorithm can be developed by object-based image segmentation and restoration algorithm. Experimental results show that the proposed object-based image restoration algorithm can efficiently remove the space-variant out of focus blur from the image with multiple blurred objects.