Semantic analysis of field sports video using a petri-net of audio-visual concepts

The most common approach to automatic summarisation and highlight detection in sports video is to train an automatic classifier to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets which can be used for both semantic description and event detection within sports videos. Low-level algorithms for the detection of perception concepts using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of perception concepts is formally defined to describe video content. We call this a Perception Concept Network-Petri Net (PCN-PN) model. Using PCN-PNs, personalized high-level semantic descriptions of video highlights can be facilitated and queries on high-level semantics can be achieved. A particular strength of this framework is that we can easily build semantic detectors based on PCN-PNs to search within sports videos and locate interesting events. Experimental results based on recorded sports video data across three types of sports games (soccer, basketball and rugby), and each from multiple broadcasters, are used to illustrate the potential of this framework

Multi-level Semantic Analysis for Sports Video

There has been a huge increase in the utilization of video as one of the most preferred type of media due to its content richness for many significant applications including sports. To sustain an ongoing rapid growth of sports video, there is an emerging demand for a sophisticated content-based indexing system. Users recall video contents in a high-level abstraction while video is generally stored as an arbitrary sequence of audio-visual tracks. To bridge this gap, this paper will demonstrate the use of domain knowledge and characteristics to design the extraction of high-level concepts directly from audio-visual features. In particular, we propose a multi-level semantic analysis framework to optimize the sharing of domain characteristics

A semantic content analysis model for sports video based on perception concepts and finite state machines

In automatic video content analysis domain, the key challenges are how to recognize important objects and how to model the spatiotemporal relationships between them. In this paper we propose a semantic content analysis model based on Perception Concepts (PCs) and Finite State Machines (FSMs) to automatically describe and detect significant semantic content within sports video. PCs are defined to represent important semantic patterns for sports videos based on identifiable feature elements. PC-FSM models are designed to describe spatiotemporal relationships between PCs. And graph matching method is used to detect high-level semantic automatically. A particular strength of this approach is that users are able to design their own highlights and transfer the detection problem into a graph matching problem. Experimental results are used to illustrate the potential of this approac

Video semantic content analysis framework based on ontology combined MPEG-7

The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. New multimedia standard, MPEG-7, provides the rich functionalities to enable the generation of audiovisual descriptions and is expressed solely in XML Schema which provides little support for expressing semantic knowledge. In this paper, a video semantic content analysis framework based on ontology combined MPEG-7 is presented. Domain ontology is used to define high level semantic concepts and their relations in the context of the examined domain. MPEG-7 metadata terms of audiovisual descriptions and video content analysis algorithms are expressed in this ontology to enrich video semantic analysis. OWL is used for the ontology description. Rules in Description Logic are defined to describe how low-level features and algorithms for video analysis should be applied according to different perception content. Temporal Description Logic is used to describe the semantic events, and a reasoning algorithm is proposed for events detection. The proposed framework is demonstrated in sports video domain and shows promising results

Extensible Detection and Indexing of Highlight Events in Broadcasted Sports Video

Content-based indexing is fundamental to support and sustain the ongoing growth of broadcasted sports video. The main challenge is to design extensible frameworks to detect and index highlight events. This paper presents: 1) A statistical-driven event detection approach that utilizes a minimum amount of manual knowledge and is based on a universal scope-of-detection and audio-visual features; 2) A semi-schema-based indexing that combines the benefits of schema-based modeling to ensure that the video indexes are valid at all time without manual checking, and schema-less modeling to allow several passes of instantiation in which additional elements can be declared. To demonstrate the performance of the events detection, a large dataset of sport videos with a total of around 15 hours including soccer, basketball and Australian football is used

A semantic event detection approach for soccer video based on perception concepts and finite state machines

A significant application area for automated video analysis technology is the generation of personalized highlights of sports events. Sports games are always composed of a range of significant events. Automatically detecting these events in a sports video can enable users to interactively select their own highlights. In this paper we propose a semantic event detection approach based on Perception Concepts and Finite State Machines to automatically detect significant events within soccer video. Firstly we define a Perception Concept set for soccer videos based on identifiable feature elements within a soccer video. Secondly we design PC-FSM models to describe semantic events in soccer videos. A particular strength of this approach is that users are able to design their own semantic events and transfer event detection into graph matching. Experimental results based on recorded soccer broadcasts are used to illustrate the potential of this approach