We address the issue of producing automatic video abstracts in the context of the video indexing of animated movies. For a quick browse of a movie's visual content, we propose a storyboard-like summary, which follows the movie's events by retaining one key frame for each specific scene. To capture the shot's visual activity, we use histograms of cumulative interframe distances, and the key frames are selected according to the distribution of the histogram's modes. For a preview of the movie's exciting action parts, we propose a trailer-like video highlight, whose aim is to show only the most interesting parts of the movie. Our method is based on a relatively standard approach, i.e., highlighting action through the analysis of the movie's rhythm and visual activity information. To suit every type of movie content, including predominantly static movies or movies without exciting parts, the concept of action depends on the movie's average rhythm. The efficiency of our approach is confirmed through several end-user studies.
In this paper we consider the problem of the automatic evaluation of the results of color image segmentation.
There are supervised evaluation criteria based on the computation of the dissimilarity measure between segmentation
result and ground truth. Also, there are unsupervised evaluation criteria that enable the quality of a
segmentation result without any a priori knowledge. Here, starting from the criteria, we retained six attributes
which are summarized in a performance vector and will be used for an evaluation based on a fuzzy neural
network.
KEYWORDS: Cameras, Video surveillance, Motion detection, Detection and tracking algorithms, Video, Visual process modeling, 3D modeling, Surveillance, Visualization, Optical tracking
Intelligent surveillance has become an important research issue due
to the high cost and low efficiency of human supervisors, and
machine intelligence is required to provide a solution for automated
event detection. In this paper we describe a real-time system that
has been used for detecting car park entries, using an adaptive
background learning algorithm and two indicators representing
activity and identity to overcome the difficulty of tracking
objects.
In order to improve the link between an operator and its machine, some human oriented communication systems are now using natural languages like speech or gesture. The goal of this paper is to present a gesture recognition system based on the fusion of measurements issued from different kind of sources. It is necessary to have some sensors that are able to capture at least the position and the orientation of the hand such as Dataglove and a video camera. Datagloge gives a measure of the hand posture and a video camera gives a measure of the general arm gesture which represents the physical and spatial properties of the gesture, and based on the 2D skeleton representation of the arm. The measurements used are partially complementary and partially redundant. The application is distributed on intelligent cooperating sensors. The paper presents the measurement of the hand and the arm gestures, the fusion processes, and the implementation solution.
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