This paper presents an automatic system for detecting and re-identifying people moving in different sites with non-overlapping views. We first propose an automatic process for silhouette extraction based on the combination of an adaptive background subtraction algorithm and a motion detection module. Such a combination takes advantage of both approaches and is able to tackle the problem of particular environments. The silhouette extraction results are then clustered based on their spatial belonging and colorimetric characteristics in order to preserve only the key regions that effectively represent the appearance of a person. The next important step consists in characterizing the extracted silhouettes by the appearance-based signatures. Our proposed descriptor, which includes both color and spatial feature of objects, leads to satisfying results compared to other descriptors in the literature. Since the passage of a person needs to be characterized by multiple frames, a large quantity of data has to be processed. Thus, a graph-based algorithm is used to realize the comparison of passages of people in front of cameras and to make the final decision of re-identification. The global system is tested on two real and difficult data sets recorded in very different environments. The experimental results show that our proposed system leads to very satisfactory results.

A method for accurate scene segmentation using two kinds of directed graph obtained by object matching and audio features is proposed. Generally, in audiovisual materials, such as broadcast programs and movies, there are repeated appearances of similar shots that include frames of the same background, object or place, and such shots are included in a single scene. Many scene segmentation methods based on this idea have been proposed; however, since they use color information as visual features, they cannot provide accurate scene segmentation results if the color features change in different shots for which frames include the same object due to camera operations such as zooming and panning. In order to solve this problem, scene segmentation by the proposed method is realized by using two novel approaches. In the first approach, object matching is performed between two frames that are each included in different shots. By using these matching results, repeated appearances of shots for which frames include the same object can be successfully found and represented as a directed graph. The proposed method also generates another directed graph that represents the repeated appearances of shots with similar audio features in the second approach. By combined use of these two directed graphs, degradation of scene segmentation accuracy, which results from using only one kind of graph, can be avoided in the proposed method and thereby accurate scene segmentation can be realized. Experimental results performed by applying the proposed method to actual broadcast programs are shown to verify the effectiveness of the proposed method.