16274304
Face detection and tracking in video using dynamic programming
Face detection and tracking are important in video content analysis since the most important objects in most video are human beings. This paper proposes a new approach for combined face detection and tracking in video. The face detection algorithm is a fast template matching procedure using iterative dynamic programming (DP). Although the face detection algorithm is designed for frontal face, the same mechanism can also be applied to track non-frontal faces with online adapted face models. Due to the essence of template matching, the algorithm is capable of comparing the similarity among different faces, which makes it suitable for tracking the same face that occur at disjointed temporal locations in video. While the proposed face detection method provides comparable accuracy as the neural network based approach, it is much faster.
2023739
Joint video scene segmentation and classification based on hidden Markov model
Video classification and segmentation are fundamental steps for efficient accessing, retrieval and browsing of large amounts of video data. We have developed a scene classification scheme using a hidden Markov model (HMM) based classifier. By utilizing the temporal behaviors of different scene classes, the HMM classifier can effectively classify video segments into one of the pre-defined scene classes. In this paper, we describe two approaches for joint video classification and segmentation based on a HMM, which works by searching for the most likely class transition path utilizing the dynamic programming technique.
17193716
Detecting news reporting using audio/visual information
This paper proposes an integrated approach to discriminate news reporting from everything else in broadcast news data based on both audio and visual information. The separation of news reporting segments from others not only can provide useful indices for video streams but also serves as a pre-processing step for tasks such as speaker identification and speech recognition so that only speech segments are passed on for further processing. A set of audio and visual features are adopted that aim at capturing the intrinsic properties of the underlying classes. Four types of classifiers (threshold fuzzy, Gaussian Mixture Model based, and Support Vector Machine) are tested. Some of the experimental results are presented and discussed in the paper.
826573
MediaAlert - a broadcast video monitoring and alerting system for mobile users
We present a system for automatic monitoring and timely dissemination of multimedia information to a range or mobile information appliances based on each user's interest profile. Multimedia processing algorithms detect and isolate relevant video segments from over twenty television broadcast programs based on a collection or words and phrases specified by the user. Content repurposing techniques are then used to convert the information into a form that is suitable for delivery to the user's mobile devices. Alerts are sent using a number of application messaging and network access protocols including email, short message service (SMS), multimedia messaging service (MMS), voice, session initiation protocol (SIP), fax, and pager protocols. The system is evaluated with respect to performance and user experiences. The MediaAlert system provides an effective and low-cost solution for the timely generation of alerts containing personal, business, and security information.
28562
Content-based indexing and retrieval-by-example in audio
This paper presents a system for audio content-based indexing and retrieval-by-example. An unsupervised method to automatically index the audio content is proposed. To realize a retrieval system where the on-line query can be performed efficiently, a new parametric distance metric is used to allow faster identification of the audio segments that are similar to the given query example. Preliminary experimental results show that the proposed new approach can serve as an effective means in content based indexing and retrieval in audio streams.
11845096
Integration of audio and visual information for content-based video segmentation
A video sequence usually consists of separate scenes, and each scene includes many shots. For video understanding purposes, it is most important to detect scene breaks. To analyze the content of each scene, detection of shot breaks is also required. Usually, a scene break is associated with a simultaneous change of image, motion, and audio characteristics, while a shot break is only accompanied with changes in image or motion or both. We propose to use audio information along with image and motion information to accomplish segmentation at different levels. Promising results have been obtained with videos digitized from TV programs.
2119274
Dynamical mobile terminal location registration in wireless PCS networks
In this paper, we propose a mobile terminal (MT) location registration/update model. In this model, the registration decision is based on two factors-the time elapsed since last call arrival and the distance the MT has traveled since last registration. It is established that the optimal registration strategy can be represented by a curve. Only when the state of the system reaches this curve is a registration performed. In order for an MT to calculate its traveled distance, an interactive implementation scheme and a distance calculation algorithm are developed. When the call interarrival times are independent and geometrically distributed, the proposed model becomes a distance-based model and, in this case, the optimal registration strategy is of threshold structure. For the distance-based model, a single sample path-based ordinal optimization algorithm is devised. In this algorithm, without any knowledge about the system parameters, the MT observes the system state transitions, estimates the ordinal of a set of strategies, and updates the registration strategy adaptively. Since only a single sample path is used, this algorithm can be implemented online. Several numerical examples are provided to compare the proposed model and the existing ones.
1176633
Consistent code generation from UML models
Relational calculus of object systems (rCOS) is an OO-language which is equipped with an observation-oriented semantics and a refinement calculus based on the Hoare and He's unifying theories of programming (UTP). In this paper, we give syntactic definitions for class diagrams and sequence diagrams in UML 2.0. Based on these definitions, we give an algorithm for checking the consistency of a class diagram and a sequence diagram. Furthermore, we develop an algorithm to generate rCOS code from any given consistent class diagram and sequence diagram.
5135154
Linking UML models of design and requirement
Both a UML model of requirement and a UML model of a design are defined as a pair of class diagram and a family of sequence diagrams. We then give an unified semantics for models of requirements and designs. We define the consistency between a design class diagram and the interaction diagrams and show how the removal of inconsistency can be treated as a model refinement. We then formally define the correctness of UML model of design with respect to the model of requirement.
4908568
Insignificant shadow detection for video segmentation
To prevent moving cast shadows from being misunderstood as part of moving objects in change detection based video segmentation, this paper proposes a novel approach to the cast shadow detection based on the edge and region information in multiple frames. First, an initial change detection mask containing moving objects and cast shadows is obtained. Then a Canny edge map is generated. After that, the shadow region is detected and removed through multiframe integration, edge matching, and region growing. Finally, a post processing procedure is used to eliminate noise and tune the boundaries of the objects. Our approach can be used for video segmentation in indoor environment. The experimental results demonstrate its good performance.
15783652
Low bit-rate video streaming for face-to-face teleconference
Face-to-face video teleconferencing is very important for real time communication. Current teleconferencing applications use standard video codecs, such as MPEG 1/2/4, for the compression of face video. Either a high bandwidth is required for high quality video transmission, or the transmitted face video is blurred at low bitrates. We present a system for real-time coding of face video at low bit-rate. There are two main contributions. First, we improve the technique of long term memory prediction by selecting frames into the database in an optimal way. A new frame is selected into the database only when it is significantly different from those frames which are already in the database. In this way, the database can cover a wider range of images. Second, we incorporate prior knowledge about faces into the long term memory prediction framework. The prior knowledge includes: (1) facial motions are repetitive such that most of them can be reconstructed from multiple reference frames; (2) different components of the face and the background can tolerate different levels of error because of different perceptual importance. Experiments show that, at similar PSNR, the proposed system works much faster and achieves better visual quality than the standard H.264/JVT codec.