720 research outputs found
Video shot boundary detection: seven years of TRECVid activity
Shot boundary detection (SBD) is the process of automatically detecting the boundaries between shots in video. It is a problem which has attracted much attention since video became available in digital form as it is an essential pre-processing step to almost all video analysis, indexing, summarisation, search, and other content-based operations. Automatic SBD was one of the tracks of activity within the annual TRECVid benchmarking exercise, each year from 2001 to 2007 inclusive. Over those seven years we have seen 57 different research groups from across the world work to determine the best approaches to SBD while using a common dataset and common scoring metrics. In this paper we present an overview of the TRECVid shot boundary detection task, a high-level overview of the most significant of the approaches taken, and a comparison of performances, focussing on one year (2005) as an example
Can we predict a riot? Disruptive event detection using Twitter
In recent years, there has been increased interest in real-world event detection using publicly accessible data made available through Internet technology such as Twitter, Facebook, and YouTube. In these highly interactive systems, the general public are able to post real-time reactions to āreal worldā events, thereby acting as social sensors of terrestrial activity. Automatically detecting and categorizing events, particularly small-scale incidents, using streamed data is a non-trivial task but would be of high value to public safety organisations such as local police, who need to respond accordingly. To address this challenge, we present an end-to-end integrated event detection framework that comprises five main components: data collection, pre-processing, classification, online clustering, and summarization. The integration between classification and clustering enables events to be detected, as well as related smaller-scale ādisruptive events,ā smaller incidents that threaten social safety and security or could disrupt social order. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely temporal, spatial, and textual content. We evaluate our framework on a large-scale, real-world dataset from Twitter. Furthermore, we apply our event detection system to a large corpus of tweets posted during the August 2011 riots in England. We use ground-truth data based on intelligence gathered by the London Metropolitan Police Service, which provides a record of actual terrestrial events and incidents during the riots, and show that our system can perform as well as terrestrial sources, and even better in some cases
Confidence Estimation and Deletion Prediction Using Bidirectional Recurrent Neural Networks
The standard approach to assess reliability of automatic speech
transcriptions is through the use of confidence scores. If accurate, these
scores provide a flexible mechanism to flag transcription errors for upstream
and downstream applications. One challenging type of errors that recognisers
make are deletions. These errors are not accounted for by the standard
confidence estimation schemes and are hard to rectify in the upstream and
downstream processing. High deletion rates are prominent in limited resource
and highly mismatched training/testing conditions studied under IARPA Babel and
Material programs. This paper looks at the use of bidirectional recurrent
neural networks to yield confidence estimates in predicted as well as deleted
words. Several simple schemes are examined for combination. To assess
usefulness of this approach, the combined confidence score is examined for
untranscribed data selection that favours transcriptions with lower deletion
errors. Experiments are conducted using IARPA Babel/Material program languages.ALTA Institute, Cambridge University; The Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) via Air Force Research Laboratory (AFRL
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