Human action recognition is the process of labeling a video according to human behavior. This process requires a large set of labeled video and analyzing all the frames of a video. The consequence is high computation and memory requirement. This paper solves these problems by focusing on a limited set rather than all the human action and considering the humanobject interaction. This paper emplo...

This paper deals with video segmentation in vision systems. We focus on the maintenance of background models in long-term videos of changing environment which is still a real challenge in video surveillance. We propose an original weakly supervised method for learning contextual variations in videos. Our approach uses a clustering algorithm to automatically identify different contexts based on ...

The fast development and increasing use of 3D video technology increases the need for standardization and certification processes when it comes to quality of video experience. Due to the specific conditions necessary for high-quality 3D video reproduction, an important role in evaluating the quality of 3D video plays the human experience factor. In order to find the best mechanisms for 3D video...

Mobile video applications have started to dominate the global mobile data traffic in recent years, and both opportunities and challenges have arisen when the emerging mobile cloud paradigm is introduced to support the resource-demanding video processing and networking services. This chapter offers in-depth discussions for contentand context-aware, adaptive, robust, secure, and real-time video a...

Distributed Video Coding (DVC) is a video coding paradigm which mainly exploits the source statistics at the decoder based on the availability of side information at the decoder. This paper considers feedback channel based Transform Domain Wyner-Ziv (TDWZ) DVC. The coding efficiency of TDWZ video coding does not match that of conventional video coding yet, mainly due to the quality of side info...

Video understanding has attracted much research attention especially since the recent availability of large-scale video benchmarks. In this paper, we address the problem of multilabel video classification. We first observe that there exists a significant knowledge gap between how machines and humans learn. That is, while current machine learning approaches including deep neural networks largely...

We address the problem of indexing video sequences according to the events they depict. While a lot’s of different approaches have been proposed in order to describe events, none is sufficiently generic and computationally efficient to be applied to event-based retrieval of video sequences within large databases. In this paper, we propose a novel index of video sequences which aims at describin...

The growing need for 'intelligent' video retrieval systems leads to new architectures combining multiple characterizations of the video content that rely on highly expressive frameworks while providing fully-automated indexing and retrieval processes. As a matter of fact, addressing the problem of combining modalities within expressive frameworks for video indexing and retrieval is of huge impo...