Event Detection and Clustering for Surveillance Video Summarization

Damnjanovic, Uros; Fernández, Virginia Luzón i; Izquierdo, Ebroul; Martinez, José M.

doi:10.1109/wiamis.2008.53

Cited by 36 publications

(22 citation statements)

References 7 publications

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“…Video summarization is therefore a challenge computer scientists, and video retrieval researchers in particular, have been trying to automatize and solve for many years. There exist many approaches presented in the literature [5,4,11], and in our context of the soccer stadium, techniques based on motion, color, clustering, event or objects should be useful [2]. However, the accuracy with respect to precision and recall have been too low, the feature extraction from the videos are too slow, and the events that may be searched for are too limited.…”

Section: Copyright Is Held By the Owner/author(s)mentioning

confidence: 99%

Automatic event extraction and video summaries from soccer games

Mortensen

Gaddam

Stensland

et al. 2014

Proceedings of the 5th ACM Multimedia Systems Conference

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Bagadus is a prototype of a soccer analysis application which integrates a sensor system, a video camera array and soccer analytics annotations. The current prototype is installed at Alfheim Stadium in Norway, and provides a large set of new functions compared to existing solutions. One important feature is to automatically extract video events and summaries from the games, i.e., an operation that traditionally consumes a huge amount of time. In this demo, we demonstrate how our integration of subsystems enable several types of summaries to be generated automatically, and we show that the video summaries are displayed with a response time around one second.

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Section: Copyright Is Held By the Owner/author(s)mentioning

confidence: 99%

Automatic event extraction and video summaries from soccer games

Mortensen

Gaddam

Stensland

et al. 2014

Proceedings of the 5th ACM Multimedia Systems Conference

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“…Several efficient video summarization approaches have been proposed for surveillance video stream such as [6]- [9]. For real-time, generally video summarization approaches utilized motion object detection and extraction as essential process to extract motion information from video sequence [10]- [12]. Many of summarization approaches generated a video synopsis or summary for a single video stream such as [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Improving Video Streams Summarization Using Synthetic Noisy Video Data

Al-Musawi¹,

Hasson²

2015

ijacsa

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Abstract-For monitoring public domains, surveillance camera systems are used. Reviewing and processing any subsequences from large amount of raw video streams is time and space consuming. Many efficient approaches of video summarization were proposed to reduce the amount of irrelevant information. Most of these approaches do not take into consideration the illumination or lighting changes that cause noise in video sequences. In this work, video summarization algorithm for video streams has been proposed using Histogram of Oriented Gradient and Correlation coefficients techniques. This algorithm has been applied on the proposed multi-model dataset which is created by combining the original data and the dynamic synthetic data. This dynamic data is proposed using Random Number Generator function. Experiments on this dataset showed the effectiveness of the proposed algorithm compared with traditional dataset.

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“…Generally, producing a video summary is useful in many applications including: surveillance [5,19], video indexing [11,32], video search [28] and retrieval [24], visualization [20], recognition [15], video compression [1], etc. From the surveillance applications point of view, millions of hours of video are captured around the world by CCTV cameras.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Although, indirectly it leads the argument into the extraction of objects [15] or events [10,26]; building meaningful semantics with scene independence has been shown to be possible. The cluster-based video summarization category exploits redundancy in characteristics or activities within image frames of the video sequence [2,5]. In spite of being efficient algorithms, these methods can neither cope with repeated segments nor handle the domination of background clusters.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Smart video summarization using mealy machine-based trajectory modelling for surveillance applications

Dogra

Ahmed

Bhaskar

2015

Multimed Tools Appl

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In this paper, we propose a smart video summarization technique that compiles a synopsis of event(s)-of-interest occurring within a segment of image frames in a video. The proposed solution space consists of extracting appropriate features that represent the dynamics of targets in surveillance environments using their motion trajectories combined with a finite state automaton model for analyzing state changes of such features to detect and localize event(s)-of-interest. We introduce the cumulative moving average (CMA) and the preceding segment average (PSA) statistical metric as features that indicate gradual and sudden changes in the instantaneous velocity of moving targets. In order to support both on-line and off-line summarization, a finite state machine, that is often referred to as Mealy Machine, has been proposed to model the trajectory of a moving target and used for detecting transitions that represents a change from one state to another when initiated by a triggering event or condition. We conduct several systematic experiments on different scenario-specific in-house videos and other publicly available datasets to demonstrate the effectiveness of our proposed approach and benchmark its performance against chosen baseline strategies. The results of our experiments highlight the superiority of our proposed Multimed Tools Appl method in accurately localizing the start and end of event(s)-of-interest in videos within the chosen dataset.

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Event Detection and Clustering for Surveillance Video Summarization

Cited by 36 publications

References 7 publications

Automatic event extraction and video summaries from soccer games

Automatic event extraction and video summaries from soccer games

Improving Video Streams Summarization Using Synthetic Noisy Video Data

Smart video summarization using mealy machine-based trajectory modelling for surveillance applications

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