33018547
Object Boundary Edge Selection for Accurate Contour Tracking Using Multi-level Canny Edges
We propose a method of selecting only tracked subject boundary edges in a video stream with changing background and a moving camera. Our boundary edge selection is done in two steps; first, remove background edges using an edge motion, second, from the output of the previous step, select boundary edges using a normal direction derivative of the tracked contour. In order to remove background edges, we compute edge motions and object motions. The edges with different motion direction than the subject motion are removed. In selecting boundary edges using the contour normal direction, we compute image gradient values on every edge pixels, and select edge pixels with large gradient values. We use multi-level Canny edge maps to get proper details of a scene. Detailed-level edge maps give us more scene information even though the tracked object boundary is not clear, because we can adjust the detail level of edge maps for a scene. We use Watersnake model to decide a new tracked contour. Our experimental results show that our approach is superior to Nguyen’s.
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.
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.
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.
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.
15751084
Decentralized user-role assignment for Web-based intranets
The intricacy of security administration is one of the most challenging problems in large networked systems. This problem is especially serious in the Web environment, which consists of synthesis of technologies and composition of various constituents. Role-Based Access Control (RBAC) can reduce the complexity and cost of security administration in large networked applications. Using RBAC itself to manage RBAC provides additional administrative convenience. The main contribution of this paper is to extend the RBAC/Web system (developed at NIST) with the URA97 model for user-role assignment (developed at GMU) to decentralize the details of RBAC administration on the Web without losing central control over the system policy.
9979797
A truncated least squares approach to the detection of specular highlights in color images
One of the most difficult aspects of dealing with illumination effects in computer vision is accounting for specularity in the images of real objects. The specular regions in an image are often saturated - which creates problem for all image processing algorithms that use decision thresholds. Such algorithms include those for edge detection, region segmentation, etc. Detecting specularity and whenever possible compensating for it are obviously advantageous. Along these lines, this paper represents a new specularity detection and compensation method which is based on the notion of truncated least-squares approximation to the function that maps the color distribution between two images of an object under different illumination conditions. We also present a protocol for the evaluation of the current method for specularity detection. Our protocol as currently formulated uses human subjects to grade the specularity detection method.
11332993
Fine-Grained and Scalable Approaches for Message Integrity
When we have multiple users compiling a single message, including shared contents, metadata, policy, and so on, the integrity of the contents created by individual users needs to be maintained in an effective manner. There is an urgent need for new mechanisms in a trusted content-sharing environment to support multiple signers for the same message, which can be dynamically updated with autonomous protection and maintenance mechanisms. In our previous work we identified and compared three different binding mechanisms including monolithic, autonomous, and chained binding mechanisms, using digital signatures. The original work was designed and implemented for digital certificates. In this paper we apply those schemes with extension to the organization-to-organization messaging services that require effective integrity verification and tracking mechanisms at the user-level in the receiving organization in a scalable manner with fine granularity. We implement our ideas in the Java remote method invocation (RMI) platform and discuss the trade-offs of the signature schemes based on our experimental results
17114395
Efficient multicast stream authentication using erasure codes
We describe a novel method for authenticating multicast packets that is robust against packet loss. Our 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. Using asymptotic techniques, we derive the authentication probability of our scheme using two different bursty loss models. A lower bound of the authentication probability is also derived for one of the loss models. To evaluate the performance of our scheme, we compare our technique with four other previously proposed schemes using empirical results.
9807251
Originator Control in Usage Control
Originator Control is an access control policy that requires recipients to gain originator’s approval for redissemination of disseminated digital object. Originator control policies are one of the generic and key concerns of usage control. Usage control is an emerging concept which encompasses traditional access control and digital rights management solutions. However, current commercial Digital Rights Management (DRM) solutions lack enforcement of access control policies such as role-based access control (RBAC), mandatory access control (MAC), discretionary access control (DAC) and originator control because their control of access to digital object is mainly based on payment. In this paper, we attempt to combine originator control policies and usage control. Then we show how this can extend traditional originator control solutions to enforce access control policies even outside of a local control environment where a central control authority is not available. License and ticket concepts are proposed and used for originator control in usage control. Also, we define seven different solution approaches to deal with various dissemination situations. In addition, we discuss some published DRM solutions and relate these to our solution approaches.