Despite considerable enhances in recognizing hand gestures from still images, there are still many challenges in the classification of hand gestures in videos. The latter comes with more challenges, including higher computational complexity and arduous task of representing temporal features. Hand movement dynamics, represented by temporal features, have to be extracted by analyzing the total fr...

The goal of video segmentation is to partition a given video sequence into smaller meaningful units, based on temporal changes in the video sequence. The task of shot boundary detection is fundamental step of video segmentation and detects the boundaries of meaningful units of video sequence called shots. This is achieved by detecting temporal discontinuities in visual content in terms of some ...

We describe a simple new technique for spatio temporal segmentation of video sequences Each pixel of a D space time video stack is mapped to a D feature point whose coordinates include three color components two motion angle components and two motion position components The clustering of these feature points provides color segmentation and motion segmentation as well as a consistent labeling of...

Predicting human attention for video is requires exploiting temporal knowledge included in the video. We propose a novel hierarchical spatial-temporal saliency model for video based on the centersurround framework using both static features and temporal features. Saliency cues are analyzed through a hierarchical segmentation model, and fused across multiple levels, yielding the spatial-temporal...

We present in this paper an original approach for content-based video segmentation using motion information. The method is generic and does not require any knowledge about the type of the processed video. Its relies on the analysis of the temporal evolution of the dynamic content of the video. The motion content is characterised by a probabilistic Gibbsian modelling of the distribution of local...

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...

Video segmentation requires separating foreground from background, but the general problem extends to more complicated scene segmentations of different objects and their multiple parts. We develop a new approach to interactive multi-label video segmentation where many objects are segmented simultaneously with consistent spatio-temporal boundaries, based on intuitive multi-colored brush scribble...

Based on a simple temporal structural model of news program, this paper presents a practical solution to automatic news story segmentation by integrating syntactic and semantic methods. First, a syntactic segmentation method is used to detect the shot boundaries in order to partition video frames into video shots. Then a semantic segmentation method based on the graph-theoretical cluster analys...

Video segmentation is different from segmentation of a single image. While several correct solutions may exist for segmenting a single image, there needs to be a consistency among segmentations of each frame for video segmentation. Previous approaches of video segmentation concentrate on motion, or combine motion and color information in a batch fashion. We propose a maximum a posteriori probab...

A new spatio-temporal segmentation approach for moving object(s) detection and tracking from a video sequence is described. Spatial segmentation is carried out using rough entropy maximization, where we use the quad-tree decomposition, resulting in unequal image granulation which is closer to natural granulation. A three point estimation based on Beta Distribution is formulated for background e...