5,440 research outputs found
Audio-coupled video content understanding of unconstrained video sequences
Unconstrained video understanding is a difficult task. The main aim of this thesis is to
recognise the nature of objects, activities and environment in a given video clip using
both audio and video information. Traditionally, audio and video information has not
been applied together for solving such complex task, and for the first time we propose,
develop, implement and test a new framework of multi-modal (audio and video) data
analysis for context understanding and labelling of unconstrained videos.
The framework relies on feature selection techniques and introduces a novel algorithm
(PCFS) that is faster than the well-established SFFS algorithm. We use the framework for
studying the benefits of combining audio and video information in a number of different
problems. We begin by developing two independent content recognition modules. The
first one is based on image sequence analysis alone, and uses a range of colour, shape,
texture and statistical features from image regions with a trained classifier to recognise
the identity of objects, activities and environment present. The second module uses audio
information only, and recognises activities and environment. Both of these approaches
are preceded by detailed pre-processing to ensure that correct video segments containing
both audio and video content are present, and that the developed system can be made
robust to changes in camera movement, illumination, random object behaviour etc. For
both audio and video analysis, we use a hierarchical approach of multi-stage
classification such that difficult classification tasks can be decomposed into simpler and
smaller tasks.
When combining both modalities, we compare fusion techniques at different levels of
integration and propose a novel algorithm that combines advantages of both feature and
decision-level fusion. The analysis is evaluated on a large amount of test data comprising
unconstrained videos collected for this work. We finally, propose a decision correction
algorithm which shows that further steps towards combining multi-modal classification
information effectively with semantic knowledge generates the best possible results
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Event detection in field sports video using audio-visual features and a support vector machine
In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable
Robust density modelling using the student's t-distribution for human action recognition
The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE
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