View Registration Using Interesting Segments of Planar Trajectories

We introduce a method for recovering the spatial and temporal alignment between two or more views of objects moving over a ground plane. Existing approaches either assume that the streams are globally synchronized, so that only solving the spatial alignment is needed, or that the temporal misalignment is small enough so that exhaustive search can be performed. In contrast, our approach can recover both the spatial and temporal alignment. We compute for each trajectory a number of interesting segments, and we use their description to form putative matches between trajectories. Each pair of corresponding interesting segments induces a temporal alignment, and defines an interval of common support across two views of an object that is used to recover the spatial alignment. Interesting segments and their descriptors are defined using algebraic projective invariants measured along the trajectories. Similarity between interesting segments is computed taking into account the statistics of such invariants. Candidate alignment parameters are verified checking the consistency, in terms of the symmetric transfer error, of all the putative pairs of corresponding interesting segments. Experiments are conducted with two different sets of data, one with two views of an outdoor scene featuring moving people and cars, and one with four views of a laboratory sequence featuring moving radio-controlled cars

Player identification in soccer videos

A method for the identification of players in soccer videos is presented. The proposed approach exploits the inherent multiple media structure of soccer videos to perform people identification without relying on face recognition. Instead, faces are detected in closeup shots, and then the filmed player is recognized by means of recognition of the number depicted on the frontal part of its jersey, or by detection and interpretation of superimposed closed caption. Players not identified by this process are then assigned to one of the labeled faces by means of a face similarity measure. We present results obtained from soccer videos of the last European Championship for national teams, held in Portugal in June 2004

Soccer players identification based on visual local features

Semantic detection and recognition of objects and events contained in a video stream has to be performed in order to provide content-based annotation and retrieval of videos. This annotation is done as a means to be able to reuse the video material at a later stage, e.g. to produce new TV programmes. A typical example is that of sports videos, where videos are annotated in order to reuse the video clips that show key highlights and players to produce short summaries for news and sports programmes. In order to select the most interesting actions among all the possibly detected highlights further analysis is required; i.e. the shots that contain a key action are typically followed by close-ups of the players that take part in the action. Therefore the automatic identification of these players would add considerable value both to the annotation and retrieval of the key highlights and key players of a sport event. The problem of detecting and recognizing faces in broadcast videos is a widely studied topic. However, in the case of soccer videos, and sports videos in general, the current techniques are not suitable for the task of face recognition, due to the high variations in pose, illumination, scale and occlusion that may happen in an uncontrolled environment. In this paper a method that copes with these problems, exploiting local features to describe a face, without requiring a precise localization of the distinguishing parts of a face, and the set of poses to describe a person and perform a more robust recognition, is presented. A similarity metric based on the number of matched interest points, able to cope with different face sizes, is also presented and experimentally validated

Automatic detection and recognition of players in soccer videos

An application for content-based annotation and retrieval of videos can be found in the sport domain, where videos are annotated in order to produce short summaries for news and sports programmes, edited reusing the video clips that show important highlights and the players involved in them. The problem of detecting and recognizing faces in broadcast videos is a widely studied topic. However, in the case of sports videos in general, and soccer videos in particular, the current techniques are not suitable for the task of face detection and recognition, due to the high variations in pose, illumination, scale and occlusion that may happen in an uncontrolled environment. In this paper we present a method for face detection and recognition, with associated metric, that copes with these problems. The face detection algorithm adds a filtering stage to the Viola and Jones Adaboost detector, while the recognition algorithm exploits i) local features to describe a face, without requiring a precise localization of the distinguishing parts of a face, and ii) the set of poses to describe a person and perform a more robust recognition

Soccer Players Identification Based on Visual Local Features

Semantic detection and recognition of objects and events contained in a video stream has to be performed in order to provide content-based annotation and retrieval of videos. This annotation is done as a means to be able to reuse the video material at a later stage, e.g. to produce new TV programmes. A typical example is that of sports videos, where videos are annotated in order to reuse the video clips that show key highlights and players to produce short summaries for news and sports programmes. In order to select the most interesting actions among all the possibly detected highlights further analysis is required; i.e. the shots that contain a key action are typically followed by close-ups of the players that take part in the action. Therefore the automatic identification of these players would add considerable value both to the annotation and retrieval of the key highlights and key players of a sport event. The problem of detecting and recognizing faces in broadcast videos is a widely studied topic. However, in the case of soccer videos, and sports videos in general, the current techniques are not suitable for the task of face recognition, due to the high variations in pose, illumination, scale and occlusion that may happen in an uncontrolled environment. In this paper a method that copes with these problems, exploiting local features to describe a face, without requiring a precise localization of the distinguishing parts of a face, and the set of poses to describe a person and perform a more robust recognition, is presented. A similarity metric based on the number of matched interest points, able to cope with different face sizes, is also presented and experimentally validated

Commercials and Trademarks Recognition

TV content is currently available through various communication channels and devices, including digital TV, mobile TV, and Internet TV. However, with the increase in TV content volume, both its management and consumption become more and more challenging. Thoroughly describing TV program analysis techniques, this book explores the systems, architectures, algorithms, applications, research results, new approaches, and open issues. Leading experts address a wide variety of related subject areas and present a scientifically sound treatment of state-of-the-art techniques for readers interested or involved in TV program analysis

Commercials and Trademarks Recognition

In this chapter we discuss the problem of detecting and recognizing the two main categories of advertisement present in television videos: explicit advertisement in the form of commercials, i.e. short video sequences advertising a product or a service, and indirect advertisement, i.e. placement of trademarks and logos. A thorough review on the current state-of-the-art algorithms and systems for commercial and trademark recognition in a variety of different video sequences, is provided. In addition, the chapter discusses an in-depth analysis of two real-time systems, one for detecting commercials, and another for trademark recognition

Commercials and Trademarks Recognition

In this chapter we discuss the problem of detecting and recognizing the two main categories of advertisement present in television videos: explicit advertisement in the form of commercials, i.e. short video sequences advertising a product or a service, and indirect advertisement, i.e. placement of trademarks and logos. A thorough review on the current state-of-the-art algorithms and systems for commercial and trademark recognition in a variety of different video sequences, is provided. In addition, the chapter discusses an in-depth analysis of two real-time systems, one for detecting commercials, and another for trademark recognition