Event detection and recognition for semantic annotation of video

Ballan, Lamberto; Bertini, Marco; Bimbo, Alberto Del; Seidenari, Lorenzo; Serra, Giuseppe

doi:10.1007/s11042-010-0643-7

Cited by 127 publications

(73 citation statements)

References 84 publications

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“…This information is often structured using an ontology, which is a formal way to represent knowledge with descriptions of concepts and relations. An advantage of using ontologies is that they provide a formal framework for supporting explicit, specific and machine-processable knowledge and provide inference and reasoning to infer implicit knowledge [3]. Several standards such as OWL (Web Ontology Language) are easy accessible.…”

Section: Query Expansion Using Knowledge Basesmentioning

confidence: 99%

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Knowledge based query expansion in complex multimedia event detection

Boer

Schutte

Kraaij

2015

Multimed Tools Appl

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A common approach in content based video information retrieval is to perform automatic shot annotation with semantic labels using pre-trained classifiers. The visual vocabulary of state-of-the-art automatic annotation systems is limited to a few thousand concepts, which creates a semantic gap between the semantic labels and the natural language query. One of the methods to bridge this semantic gap is to expand the original user query using knowledge bases. Both common knowledge bases such as Wikipedia and expert knowledge bases such as a manually created ontology can be used to bridge the semantic gap. Expert knowledge bases have highest performance, but are only available in closed domains. Only in closed domains all necessary information, including structure and disambiguation, can be made available in a knowledge base. Common knowledge bases are often used in open domain, because it covers a lot of general information. In this research, query expansion using common knowledge bases ConceptNet and Wikipedia is compared to an expert description of the topic applied to content-based information retrieval of complex events. We run experiments on the Test Set of TRECVID MED 2014. Results show that 1) Query Expansion can improve performance compared to using no query expansion in the case that the main noun of the query could not be matched to a concept detector; 2) Query expansion using expert knowledge is not necessarily better than query expansion using common knowledge; 3) ConceptNet performs slightly better than Wikipedia; 4) Late fusion can slightly improve performance. To conclude, query expansion has potential in complex event detection.

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Section: Query Expansion Using Knowledge Basesmentioning

confidence: 99%

“…Complex or high-level events are defined as 'long-term spatially and temporally dynamic object interactions that happen under certain scene settings' [20] or 'something happening at a given time and in a given location' [3]. Research regarding complex event detection and the semantic gap increased with the benchmark TRECVID.…”

Section: Complex Event Detectionmentioning

confidence: 99%

Knowledge based query expansion in complex multimedia event detection

Boer

Schutte

Kraaij

2015

Multimed Tools Appl

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“…Lavee et al [67] reviewed event modeling methods, mostly in the context of simple human activity analysis. A review more related to this paper is the one by Ballan et al [8], which discussed features and models for detecting both simple actions and complex events in videos.…”

Section: Related Reviewsmentioning

confidence: 99%

High-level event recognition in unconstrained videos

Jiang

Bhattacharya

Chang

et al. 2012

Int J Multimed Info Retr

160

108

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The goal of high-level event recognition is to automatically detect complex high-level events in a given video sequence. This is a difficult task especially when videos are captured under unconstrained conditions by nonprofessionals. Such videos depicting complex events have limited quality control, and therefore, may include severe camera motion, poor lighting, heavy background clutter, and occlusion. However, due to the fast growing popularity of such videos, especially on the Web, solutions to this problem are in high demands and have attracted great interest from researchers. In this paper, we review current technologies for complex event recognition in unconstrained videos. While the existing solutions vary, we identify common key modules and provide detailed descriptions along with some insights for each of them, including extraction and representation of low-level features across different modalities, classification strategies, fusion techniques, etc. Publicly available benchmark datasets, performance metrics, and related research forums are also described. Finally, we discuss promising directions for future research.

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“…In the last few years, high-level event detection and description in videos has drawn increasing interest from the scientific community [2]. Video surveillance systems, advanced human-computer interfaces and semantic video indexing are just the main examples of the possible application fields of this research branch.…”

Section: Event Detection In Underwater Scenesmentioning

confidence: 99%

“…Video surveillance systems, advanced human-computer interfaces and semantic video indexing are just the main examples of the possible application fields of this research branch. However, the video event recognition task has often been tackled in a domain-specific way (except few exceptions, such as [14,42])-where the typical domains are for example sports, movies, video-surveillance and user-generated content-which caused the lack of a general detection framework [2]. The most popular application fields for event detection in videos are sports [36], video-surveillance [1], road traffic control [55].…”

Section: Event Detection In Underwater Scenesmentioning

confidence: 99%

Understanding fish behavior during typhoon events in real-life underwater environments

Spampinato

Palazzo

Boom

et al. 2012

Multimed Tools Appl

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The study of fish populations in their own natural environment is a task that has usually been tackled in invasive ways which inevitably influenced the behavior of the fish under observation. Recent projects involving the installation of permanent underwater cameras (e.g. the Fish4Knowledge (F4K) project, for the observation of Taiwan's coral reefs) allow to gather huge quantities of video data, without interfering with the observed environment, but at the same time require the development of automatic processing tools, since manual analysis would be impractical for such amounts of videos. Event detection is one of the most interesting This research was funded by European Commission FP7 grant 257024, for the Fish4Knowledge project (www.fish4knowledge.eu). Appl (2014) 70:199-236 aspects from the biologists' point of view, since it allows the analysis of fish activity during particular events, such as typhoons. In order to achieve this goal, in this paper we present an automatic video analysis approach for fish behavior understanding during typhoon events. The first step of the proposed system, therefore, involves the detection of "typhoon" events and it is based on video texture analysis and on classification by means of Support Vector Machines (SVM). As part of our behavior understanding efforts, trajectory extraction and clustering have been performed to study the differences in behavior when disruptive events happen. The integration of event detection with fish behavior understanding surpasses the idea of simply detecting events by low-level features analysis, as it supports the full semantic comprehension of interesting events.

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Event detection and recognition for semantic annotation of video

Cited by 127 publications

References 84 publications

Knowledge based query expansion in complex multimedia event detection

Knowledge based query expansion in complex multimedia event detection

High-level event recognition in unconstrained videos

Understanding fish behavior during typhoon events in real-life underwater environments

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