A comprehensive study of visual event computing

Wan, Yan; Kieran, Declan; Rafatirad, Setareh; Jain, Ramesh

doi:10.1007/s11042-010-0560-9

Cited by 18 publications

(10 citation statements)

References 93 publications

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“…As future work, we would like to develop a reasoning and decision-making unit that exploits the global knowledge about objects to detect events [16]. If we can model the different situations to detect, this module could trigger alarms and select which information is offered to the surveillance staff, in order to avoid them being overwhelmed with the vast amount of events that can happen at the same time.…”

Section: Discussionmentioning

confidence: 99%

Smart video sensors for 3D scene reconstruction of large infrastructures

Ripollés

Simó

Benet

et al. 2012

Multimed Tools Appl

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This paper introduces a new 3D-based surveillance solution for large infrastructures. Our proposal is based on an accurate 3D reconstruction using the rich information obtained from a network of intelligent video-processing nodes. In this manner, if the scenario to cover is modeled in 3D with high precision, it will be possible to locate the detected objects in the virtual representation. Moreover, as an improvement over previous 2D solutions, having the possibility of modifying the view point enables the application to choose the perspective that better suits the current state of the scenario. In this sense, the contextualization of the events detected in a 3D environment can offer a much better understanding of what is happening in the real world and where it is exactly happening. Details of the video processing nodes are given, as well as of the 3D reconstruction tasks performed afterwards. The possibilities of such a system are described and the performance obtained is analyzed.

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Section: Discussionmentioning

confidence: 99%

Smart video sensors for 3D scene reconstruction of large infrastructures

Ripollés

Simó

Benet

et al. 2012

Multimed Tools Appl

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“…Event is defined as a semantic unit which bridges the gap between semantic world and cyberspace. [19] An event has the basic components such as who(object), when(time stamp), where(site), what(description), and why(reasoning). As a fundamental structure, discrete events could be stored in computers as logs for the purposes of analysis and archiving.…”

Section: Fig 2 An Example Of a Perceptual Human Face In Dotsmentioning

confidence: 99%

“…Albeit we do not exactly affirm the colors of the photo, we still could map the colors of this scene of today to the gray scale image using color transferring technologies based on texture synthesis. [1] [3] Video analogy was derived from image analogy [19][4]. Assume we have two similar videos at hand, we therefore create a relationship and bridge the gap between two videos.…”

Section: Related Workmentioning

confidence: 99%

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Event Analogy Based Privacy Preservation in Visual Surveillance

Yan

Liu

2016

Image and Video Technology – PSIVT 2015 Workshops

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Abstract. Privacy preservation as a thorny problem in surveillance has been arisen because of its relevance to human right, however it has not been completely solved yet today. In this paper, we investigate this existing problem and expect to get ride of those intuitive methods such as pixelization, blurring or mosaicking on human face regions through object tracking. We detail privacy preservation at event level and thereafter choose suitable events represented by motion pictures in virtual reality to replace those events of surveillance in real reality. The advantage of taking use of this approach is to leverage the utility and privacy of surveillance events. The outcome will not affect visual effects and surveillance security much however it is able to achieve the objectives of privacy preservation.

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“…They can be classified in two groups: event clustering approaches [5][6][7][8][9][10][11] and event hybrid approaches [12,14,17,21,27,28]. Extracting events from multimedia in terms of photographs or images is much more difficult when compared to text for essentially two reasons: i) Event detection from images requires aggregation of heterogeneous metadata [29]; ii) Linking multimedia data to event model aspects is far more challenging then textual data [30]. In fact, many aspects of an event should be taken into consideration, as described in the multimedia event model presented in [13], such as time, space, actors, granularities, sub-events, etc.…”

Section: B Event Detection From Multimediamentioning

confidence: 99%

Foto2Events: From Photos to Event Discovery and Linking in Online Social Networks

Raad

Chbeir

2014

2014 IEEE Fourth International Conference on Big Data and Cloud Computing

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Online social networking has become the predominant activity in the digital world thanks to multimedia data (mainly photos) sharing (e.g., photos now represent 93% of the top posts on Facebook). Discovering events where users are involved using their own posts and those shared by their friends would be of great importance. In this paper, we address this issue by providing an original approach able to detect, enrich and also link user's events using photos shared within his online social networks. Using metadata, our approach provides a multidimensional gathering of similar photos using their temporal, geographical, and social facets. To validate our approach, we implemented a prototype called Foto2Events and conducted a set of experiments on real and generated data. Results show that our approach works well for various metadata distributions.

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A comprehensive study of visual event computing

Cited by 18 publications

References 93 publications

Smart video sensors for 3D scene reconstruction of large infrastructures

Smart video sensors for 3D scene reconstruction of large infrastructures

Event Analogy Based Privacy Preservation in Visual Surveillance

Foto2Events: From Photos to Event Discovery and Linking in Online Social Networks

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