Video structure extraction is essential to automatic and contentbased organization, retrieval and browsing of video. However, while many robust shot segmentation algorithms have developed, it is still difficult to extract scene structures or group shots into scenes. In this paper, we present a novel audio assisted video segmentation scheme, in which audio and color information is integrated in ...

In this chapter we explain wavelet based moving object detection and segmentation in video frames. Starting from discrete wavelet transform (DWT), we show the recent developments employing multi-wavelets (MW), and later on we switch to the use of dual-tree complex wavelet transform (DT-CWT). Working on a video instead of an image requires more attention, and at the same time, proposes novel app...

Video Object Segmentation, and video processing in general, has been historically dominated by methods that rely on the temporal consistency and redundancy in consecutive video frames. When the temporal smoothness is suddenly broken, such as when an object is occluded, or some frames are missing in a sequence; the result of these methods can deteriorate significantly or they may not even produc...

Very low bit-rate coding requires new paradigms that go well beyond pixeland frame-based video representations. We introduce a novel content-based video representation using tridimensional entities: textured object models and pose estimates. The multiproperty object models carry stochastic information about the shape and texture of each object present in the scene. The pose estimates define the...

Video segmentation is fundamental to a number of applications related to video retrieval and analysis. To realize the content based video retrieval, the video information should be organized to elaborate the structure of the video. The segmentation video into shot is an important step to make. This paper presents a new method of shot boundaries detection based on motion activities in video sequ...

Video, a rich information source, is commonly used for capturing and sharing knowledge in learning systems. However, the unstructured and linear features of video introduce difficulties for end users in accessing the knowledge captured in videos. To extract the knowledge structures hidden in a lengthy, multi-topic lecture video and thus make it easily accessible, we need to first segment the vi...

Video object segmentation and tracking are two essential building blocks of smart surveillance systems. However, there are several issues that need to be resolved. Threshold decision is a difficult problem for video object segmentation with a multibackground model.Addition to , some conditions make robust video object tracking difficult. These conditions include nonrigid object motion, target a...

Deep learning has been demonstrated to achieve excellent results for image classification and object detection. However, the impact of deep learning on video analysis (e.g. action detection and recognition) has not been that significant due to complexity of video data and lack of annotations. In addition, training deep neural networks on large scale video datasets is extremely computationally e...

We present an approach for object segmentation in videos that combines frame-level object detection with concepts from object tracking and motion segmentation. The approach extracts temporally consistent object tubes based on an off-the-shelf detector. Besides the class label for each tube, this provides a location prior that is independent of motion. For the final video segmentation, we combin...

Remote accessibility to video cameras, face recognition software, and searchable image and video databases enable automated video surveillance. The same technologies and features enabling advanced functionalities facilitate as well the misuse of a surveillance system and can lead to the infringement of the privacy of an individual. Examples of misuse are voyeurism and the unauthorized collectio...