Ka Ki Ng

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This paper presents new methods for efficient object tracking in video sequences using multiple features and particle filtering. A histogram-based framework is used to describe the features. Histograms are useful because have the property that they allow changes in the object appearance while the histograms remain the same. Particle filtering is used(More)
In this paper we present new methods for object tracking initialization using automated moving object detection based on background subtraction. The new methods are integrated into the real-time object tracking system we previously proposed. Our proposed new background model updating method and adaptive thresholding are used to produce a foreground object(More)
ii ACKNOWLEDGMENTS Upon the completion of this dissertation, I realized that there will never be the perfect words to express my gratitude towards those who have inspired, encouraged and motivated me through my years at Purdue. Yet, these names and faces will always remain deep in my heart. First of all, I would like to thank my major advisor, Professor(More)
Behavioral information can be inferred from the trajectory of a rigid object. We propose a method to detect anomalies in the approach of a vehicle by observing the patterns in its velocity and describe methods for more effective analysis of the velocity trajectory. First we define a hypothetical coordinate system in which the axes are specified with respect(More)
Crowd estimation and monitoring is an important surveillance task. We address the problem of estimating the " flow, " that is the number of persons passing a designated region in a unit time. We designate an area of the scene as a virtual trip wire and accumulate the total number of foreground pixels (in the trip wire) over a chosen time period. We show(More)
In this paper, we propose a simple method for foreground segmentation based on a " Gaussianity test " and a shading model. The proposed method works under a hierarchical framework that combines a block based and a pixel based processing. The first step is a block-level classification based on the intensity differences and intensity ratios of a background(More)
In this paper, we present a novel method for moving object detection using background subtraction and tracking for lightweight visual surveillance systems. The proposed moving object detection method using background subtraction is based on a " Gaussianity test " and a shading model. The method has been shown to be robust to dynamic scenes such as sudden(More)
In this paper we present a new method for object tracking initialization using background subtraction. We propose an effective scheme for updating a background model adaptively in dynamic scenes. Unlike the traditional methods that use the same " learning rate " for the entire frame or sequence, our method assigns a learning rate for each pixel according to(More)
Modern portfolio theory dates back to a seminal 1952 paper by H. Markowitz and has been very influential both in academic finance and among practitioners in the financial industry. Given a set of assets, the theory can be used to compute the amount to be invested in each asset in order to construct an optimally diversified portfolio. One of the parameters(More)
Color is a powerful attribute that is used to characterize objects for tracking and other surveillance tasks. Since color is dependent on ambient illumination and the imaging equipment, the reliability of color features decreases as the object moves through regions observed with different cameras and with different illumination conditions. Typically, this(More)