Ebroul Izquierdo

A Probabilistic Approach for Vision-Based Fire Detection in Videos

Automated fire detection is an active research topic in computer vision. In this paper, we propose and analyze a new method for identifying fire in videos. Computer vision-based fire detection algorithms are usually applied in closed-circuit television surveillance scenarios with controlled background. In contrast, the proposed method can be applied not only to surveillance but also to automatic video classification for retrieval of fire catastrophes in databases of newscast content. In the latter case, there are large variations in fire and background characteristics depending on the video instance. The proposed method analyzes the frame-to-frame changes of specific low-level features describing potential fire regions. These features are color, area size, surface coarseness, boundary roughness, and skewness within estimated fire regions. Because of flickering and random characteristics of fire, these features are powerful discriminants. The behavioral change of each one of these features is evaluated, and the results are then combined according to the Bayes classifier for robust fire recognition. In addition, a priori knowledge of fire events captured in videos is used to significantly improve the classification results. For edited newscast videos, the fire region is usually located in the center of the frames. This fact is used to model the probability of occurrence of fire as a function of the position. Experiments illustrated the applicability of the method.

Video streaming over P2P networks: Challenges and opportunities

A robust real-time video communication service over the Internet in a distributed manner is an important challenge, as it influences not only the current Internet structure but also the future Internet evolution. In this context, Peer-to-Peer (P2P) networks are playing an imperative position for providing efficient video transmission over the Internet. Recently, several P2P video transmission systems have been proposed for live video streaming services or video-on-demand services over the Internet. In this paper, we describe and discuss existing video streaming systems over P2P. Efficient (delay tolerant and intolerant) data sharing mechanisms in P2P and current video coding trends are elaborated in detail. Moreover, video streaming solutions (live and on-demand) over P2P from the perspective of tree-based and mesh-based systems are explained. Finally, the conclusion is drawn with key challenges and open issues related to video streaming over P2P.

An Object- and User-Driven System for Semantic-Based Image Annotation and Retrieval

In this paper, a system for object-based semi-automatic indexing and retrieval of natural images is introduced. Three important concepts underpin the proposed system: a new strategy to fuse different low-level content descriptions; a learning technique involving user relevance feedback; and a novel object based model to link semantic terms and visual objects. To achieve high accuracy in the retrieval and subsequent annotation processes several low-level image primitives are combined in a suitable multifeatures space. This space is modelled in a structured way exploiting both low-level features and spatial contextual relations of image blocks. Support vector machines are used to learn from gathered information through relevance feedback. An adaptive convolution kernel is defined to handle the proposed structured multifeature space. The positive definite property of the introduced kernel is proven, as essential condition for uniqueness and optimality of the convex optimization in support vector machines. The proposed system has been thoroughly evaluated and selected results are reported in this paper

Peer-to-peer streaming of scalable video in future Internet applications

Scalable video delivery over peer-to-peer networks appears to be key for efficient streaming in emerging and future Internet applications. Contrasting the conventional server-client approach, here, video is delivered to a user in a fully distributed fashion. This is, for instance, beneficial in cases where a high demand for a particular video content is imposed, as different users can receive the same data from different peers. Furthermore, due to the heterogeneous nature of Internet connectivity, the content needs to be delivered to a user through networks with highly varying bandwidths. Moreover, content needs to be displayed on a variety of devices featuring different sizes, resolutions, and computational capabilities. If video is encoded in a scalable way, it can be adapted to any required spatio-temporal resolution and quality in the compressed domain, according to a peer bandwidth and other peers¿ context requirements. This enables efficient low-complexity content adaptation and interoperability for improved peer-to-peer streaming in future Internet applications. An efficient piece picking and peer selection policy enables high quality of service in such a streaming system.

Image Classification using Chaotic Particle Swarm Optimization

Particle swarm optimization is one of several meta-heuristic algorithms inspired by biological systems. The chaotic modeling of particle swarm optimization is presented in this paper with application to image classification. The performance of this modified particle swarm optimization algorithm is compared with standard particle swarm optimization. Numerical results of this comparative study are performed on binary classes of images from the Corel dataset.

H.264/AVC to HEVC Video Transcoder Based on Dynamic Thresholding and Content Modeling

The new video coding standard, High Efficiency Video Coding (HEVC), was developed to succeed the current standard, H.264/AVC, as the state of the art in video compression. However, there is a lot of legacy content encoded with H.264/AVC. This paper proposes and evaluates several transcoding algorithms from the H.264/AVC to the HEVC format. In particular, a novel transcoding architecture, in which the first frames of the sequence are used to compute the parameters so that the transcoder can learn the mapping for that particular sequence, is proposed. Then, two types of mode mapping algorithms are proposed. In the first solution, a single H.264/AVC coding parameter is used to determine the outgoing HEVC partitions using dynamic thresholding. The second solution uses linear discriminant functions to map the incoming H.264/AVC coding parameters to the outgoing HEVC partitions. This paper contains experiments designed to study the impact of the number of frames used for training in the transcoder. Comparisons with existing transcoding solutions reveal that the proposed work results in lower rate-distortion loss at a competitive complexity performance.

Subjective evaluation of scalable video coding for content distribution

This paper investigates the influence of the combination of the scalability parameters in scalable video coding (SVC) schemes on the subjective visual quality. We aim at providing guidelines for an adaptation strategy of SVC that can select the optimal scalability options for resource-constrained networks. Extensive subjective tests are conducted by using two different scalable video codecs and high definition contents. The results are analyzed with respect to five dimensions, namely, codec, content, spatial resolution, temporal resolution, and frame quality.

Image-based rendering and 3D modeling: A complete framework

Abstract Multi-viewpoint synthesis of video data is a key technology for the integration of video and 3D graphics, as necessary for telepresence and augmented-reality applications. This paper describes a number of important techniques which can be employed to accomplish that goal. The techniques presented are based on the analysis of 2D images acquired by two or more cameras. To determine depth information of single objects present in the scene, it is necessary to perform segmentation and disparity estimation. It is shown, how these analysis tools can benefit from each other. For viewpoint synthesis, techniques with different levels of tradeoff between complexity and degrees of freedom are presented. The first approach is disparity-controlled view interpolation, which is capable of generating intermediate views along the interocular axis between two adjacent cameras. The second is the recently introduced incomplete 3D technique, which in a first step extracts the texture of the visible surface of a video object acquired with multiple cameras, and then performs disparity-compensated projection from the surface onto a view plane. In the third and most complex approach, a 3D model of the object is generated, which can be represented by a 3D wire grid. For synthesis, this model can be rotated to arbitrary orientations, and original texture is mapped onto the surface to obtain an arbitrary view of the processed object. The result of this rendering procedure is a virtual image with very natural appearance.

A complexity-scalable transcoder from H.264/AVC to the new HEVC codec

The emerging video coding standard, HEVC, is currently approaching the final stage of development prior to standardization. However, the current H.264/AVC standard is very successful, and it has been widely adopted for many applications. Thus, transcoding between these codecs will be highly needed once the HEVC codec is finalised. This paper studies the performance of one of the most common techniques for heterogeneous transcoding, motion vector (MV) reuse, in a H.264/AVC to HEVC transcoder. Furthermore, it proposes a new transcoder that is capable of complexity scalability, trading off rate-distortion performance for complexity reduction. The proposed transcoder is based on a new metric to compute the similarity of the H.264/AVC MVs, which is used to decide which HEVC partitions are tested on the transcoder.

MPEG-2 to HEVC Video Transcoding With Content-Based Modeling

This paper proposes an efficient MPEG-2 to High Efficiency Video Coding (HEVC) video transcoder. The objective of the transcoder is to migrate the abundant MPEG-2 video content to the emerging HEVC video coding standard. The transcoder introduces a content-based machine learning solution to predict the depth of the HEVC coding units. The proposed transcoder utilizes full re-encoding to find a mapping between the incoming MPEG-2 coding information and the outgoing HEVC depths of the coding units. Once the model is built, a switch to transcoding mode occurs. Hence, the model is content based and varies from one video sequence to another. The transcoder is compared against full re-encoding using the default HEVC fast motion estimation. Using HEVC test sequences, it is shown that a speedup factor of up to 3 is achieved, while reducing the bitrate of the incoming video by around 50%. In comparison to full re-encoding, an average of 3.9% excessive bitrate is encountered with an average PSNR drop of 0.1 dB. Since this is the first work to report on MPEG-2 to HEVC video transcoding, the reported results can be used as a benchmark for future transcoding research.