44345092
Accurate object contour tracking based on boundary edge selection
In this paper, a novel method for accurate subject tracking, by selecting only tracked subject boundary edges in a video stream with a changing background and moving camera, is proposed. This boundary edge selection is achieved in two steps: (1) removing background edges using edge motion, and from the output of the previous step, (2) selecting boundary edges using a normal direction derivative of the tracked contour. Accurate tracking is based on reduction of the effects of irrelevant edges, by only selecting boundary edge pixels. In order to remove background edges using edge motion, the tracked subject motion is computed and edge motions and edges having different motion directions from the subjects are removed. In selecting boundary edges using the normal contour direction, the image gradient values on every edge pixel are computed, and edge pixels with large gradient values are selected. Multi-level Canny edge maps are used to obtain proper details of a scene. Multi-level edge maps allow tracking, even though the tracked object boundary has complex edges, since the detail level of an edge map for the scene can be adjusted. A process of final routing is deployed in order to obtain a detailed contour. The computed contour is improved by checking against a strong Canny edge map and hiring strong Canny edge pixels around the computed contour using Dijkstra's minimum cost routing. The experimental results demonstrate that the proposed tracking approach is robust enough to handle a complex-textured scene in a mobile camera environment.
52850595
LOD Canny Edge Based Boundary Edge Selection for Human Body Tracking
We propose a simple method for tracking a nonparameterized subject contour in a single video stream with a moving camera and changing background. Our method is based on level-of-detail (LOD) Canny edge maps and graph-based routing operations on the LOD maps. LOD Canny edge maps are generated by changing scale parameters for a given image. Simple (strong) Canny edge map has the smallest number of edge pixels while the most detailed Canny edge map, Wcanny N , has the biggest number of edge pixels. We start our basic tracking using strong Canny edges generated from large image intensity gradients of an input image, called Scanny edges to reduce side-effects because of irrelevant edges. Starting from Scanny edges, we get more edge pixels ranging from simple Canny edge maps until the most detailed Canny edge maps. LOD Canny edge pixels become nodes in routing, and LOD values of adjacent edge pixels determine routing costs between the nodes. We find a best route to follow Canny edge pixels favoring stronger Canny edge pixels. Our accurate tracking is based on reducing effects from irrelevant edges by selecting the stronger edge pixels, thereby relying on the current frame edge pixel as much as possible contrary to other approaches of always combining the previous contour. Our experimental results show that this tracking approach is robust enough to handle a complex-textured scene.
62242004
Object boundary edge selection using normal direction derivatives of a contour in a complex scene
Nguyen proposed a method [H.T., Nguyen et al., 2002] for tracking a nonparameterized object (subject) contour in a single video stream. Nguyen's approach combined outputs of two steps: creating a predicted contour and removing background edges. We propose a method to increase object tracking accuracy by improving the background edge removal process. Nguyen's background edge removal method of leaving many irrelevant edges is subject to inaccurate contour tracking. Our accurate tracking is based on reducing affects from irrelevant edges by selecting the boundary edge only. We select high-valued edge pixels of average image intensity gradients in the contour normal direction. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.
8794763
Video Retrieval of Human Interactions Using Model-Based Motion Tracking and Multi-layer Finite State Automata
Recognition of human interactions in a video is useful for video annotation, automated surveillance, and content-based video retrieval. This paper presents a model-based approach to motion tracking and recognition of human interactions using multi-layer finite state automata (FA). The system is used for widely-available, static-background monocular surveillance videos. A three-dimensional human body model is built using a sphere and cylinders and is projected on a two-dimensional image plane to fit the foreground image silhouette. We convert the human motion tracking problem into a parameter optimization problem without the need to compute inverse kinematics. A cost functional is used to estimate the degree of the overlap between the foreground input image silhouette and a projected three-dimensional body model silhouette. Motion data obtained from the tracker is analyzed in terms of feet, torso, and hands by a behavior recognition system. The recognition model represents human behavior as a sequence of states that register the configuration of individual body parts in space and time. In order to overcome the exponential growth of the number of states that usually occurs in single-level FA, we propose a multi-layer FA that abstracts states and events from motion data at multiple levels: low-level FA analyzes body parts only, and high-level FA analyzes the human interaction. Motion tracking results from video sequences are presented. Our recognition framework successfully recognizes various human interactions such as approaching, departing, pushing, pointing, and handshaking.
34356748
Model-Based Human Motion Capture from Monocular Video Sequences
Generating motion and capturing motion of an articulated body for computer animation is an expensive and time-consuming task. Conventionally, animators manually generate intermediate frames between key frames, but this task is very labor-intensive. This paper presents a model-based singularity-free automatic-initialization approach to capturing human motion from widely-available, static background monocular video sequences. A 3D human body model is built and projected on a 2D projection plane to find the best fit with the foreground image silhouette. We convert the human motion capture problem into two types of parameter optimization problems: static optimization and dynamic optimization. First, we determine each model body configuration using static optimizations for every input image. Then, to obtain better description of motion, the results from all static optimizations are fed into a dynamic optimization process where the entire sequence of motion is considered for the user-specified motion. The user-specified motion is defined by the user and the final form of the motion they want. A cost function for static optimization is used to estimate the degree of overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. A cost function for dynamic optimization is the user-specified motion based on the static optimization results as well as image fitting. Our method is used to capture various human motions: walking, pushing, kicking, and handshaking.
5761929
Dual-beam structured-light scanning for 3-D object modeling
In this paper we present our Dual-Beam Structured-Light Scanner (DSLS), a scanning system that generates range maps much richer than those obtained from a conventional structured-light scanning system. Range maps produced by DSLS require fewer registrations for 3-D modeling. We show that the DSLS system more easily satisfies what are often difficult-to-satisfy conditions for determining the 3-D coordinates of an arbitrary object point. Two specific advantages of DSLS over conventional structured-light scanning are: (1) A single scan by the DSLS system is capable of generating range data on more surfaces than possible with the conventional approach using the same number of camera images. And (2) since the data collected by DSLS is more free of self-occlusions, the object needs be examined from a smaller number of viewpoints.
799734
Efficient multicast packet authentication using signature amortization
We describe a novel method for authenticating multicast packets that is robust against packet loss. Our main focus is to minimize the size of the communication overhead required to authenticate the packets. Our approach is to encode the hash values and the signatures with Rabin's Information Dispersal Algorithm (IDA) to construct an authentication scheme that amortizes a single signature operation over multiple packets. This strategy is especially efficient in terms of space overhead, because just the essential elements needed for authentication (i.e., one hash per packet and one signature per group of packets) are used in conjunction with an erasure code that is space optimal. To evaluate the performance of our scheme, we compare our technique with four other previously proposed schemes using analytical and empirical results. Two different bursty loss models are considered in the analyses.
6379048
Secure Cookies on the Web
Web servers and browsers typically use cookies to capture information for subsequent communications, which provides continuity and state across HTTP connections. At present, Internet e-commerce is somewhat limited in using cookies because sensitive information cannot be securely stored and communicated in typical cookies. Secure cookies offer a potential solution to this problem.
1384536
Mining association rules with adjustable accuracy
In this paper, we devise efficient algorithms for mining association rules with adjustable accuracy. It is noted that several applications require mining the transaction data to capture the customer behavior frequently. In those applications, the efficiency of data mining could be a more important faktor t.han the requirement for complete accuracy of the mining results. Allowing imprecise results can significantly improve the data mining efficiency. In this paper, two methods for mining association rules with adjustable accuracy are developed. By dealing with the concept of sampling, both methods obtain some essential knowledge from a sampled subset first, and in light of that knowledge, perform efficient association rule mining on the entire database. A technique of relaxing the support factor based on the sampling size is devised to achieve the desired level of accuracy. These two methods differ from each other in their ways of utilizing the sampled data. Performance of these two methods is comparatively analyzed. As shown by our experimental results, the relaxation factor, as well as the sample size, can be properly adjusted so as to improve the result accuracy while minimizing the corresponding execution time, thereby allowing us to effectively achieve a design trade-off between accuracy and efficiency with two control parameters. It is shown that with the advantage of controlled sampling, the proposed methods are very flexible and efficient, and can in general lead to results of a very high degree of accuracy.
17981428
SECK: survivable and efficient clustered keying for wireless sensor networks
A wireless sensor network (WSN) typically consists of a large number of small sensor nodes and one or more high-end control and data aggregation nodes. Sensor nodes have limited computation and communication capabilities, and communicate via wireless links. Consequently, WSNs are highly vulnerable to attacks. This vulnerability is exacerbated when WSNs have to operate unattended in a hostile environment, such as battlefields. In this paper, we propose a novel self-organizing key management scheme for large-scale WSNs, called Sunivable and efficient clustered keying (SECK). Our scheme was designed specifically to address the key management issues within the low-tier of a hierarchical network architecture. Previous approaches for WSN key management adequately addressed operational issues, but to a large extent, ignored robustness and recoverability issues. Using simulation and analysis, we show that SECK is highly robust against key and node captures, and has noteworthy advantages over other key management schemes.