11,849 research outputs found
Is a Technological Singularity near also for bots in MMOGs?
Using the idea of the Technological Singularity this essay offers some reflections on the possible future of bots in Massively Multiplayer Online Games (MMOGs). The paper starts by briefly introducing the notion of Technological Singularity as the advent of a super-intelligent Artificial Intelligence that could threaten human existence. Bots are computer programs that automate repetitive and time consuming activities for the Internet user. In MMOGs, bots are often used to cheat and could have nefarious effects on the gameplay. Assuming that bots are rudimentary forms of Artificial Intelligence that also pose a threat to MMOGs and their players, the paper presents some evidence-based trends of the future evolution of bots and the implications of these for Virtual Worlds research
Statistical Analysis of Dynamic Actions
Real-world action recognition applications require the development of systems which are fast, can handle a large variety of actions without a priori knowledge of the type of actions, need a minimal number of parameters, and necessitate as short as possible learning stage. In this paper, we suggest such an approach. We regard dynamic activities as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences which captures the similarities in their behavioral content. This measure is nonparametric and can thus handle a wide range of complex dynamic actions. Having a behavior-based distance measure between sequences, we use it for a variety of tasks, including: video indexing, temporal segmentation, and action-based video clustering. These tasks are performed without prior knowledge of the types of actions, their models, or their temporal extents
Video Storytelling: Textual Summaries for Events
Bridging vision and natural language is a longstanding goal in computer
vision and multimedia research. While earlier works focus on generating a
single-sentence description for visual content, recent works have studied
paragraph generation. In this work, we introduce the problem of video
storytelling, which aims at generating coherent and succinct stories for long
videos. Video storytelling introduces new challenges, mainly due to the
diversity of the story and the length and complexity of the video. We propose
novel methods to address the challenges. First, we propose a context-aware
framework for multimodal embedding learning, where we design a Residual
Bidirectional Recurrent Neural Network to leverage contextual information from
past and future. Second, we propose a Narrator model to discover the underlying
storyline. The Narrator is formulated as a reinforcement learning agent which
is trained by directly optimizing the textual metric of the generated story. We
evaluate our method on the Video Story dataset, a new dataset that we have
collected to enable the study. We compare our method with multiple
state-of-the-art baselines, and show that our method achieves better
performance, in terms of quantitative measures and user study.Comment: Published in IEEE Transactions on Multimedi
Automatic Assessment of the Type and Intensity of Agitated Hand Movements
With increasing numbers of people living with dementia, there is growing interest in the automatic monitoring of agitation. Current assessments rely on carer observations within a framework of behavioural scales. Automatic monitoring of agitation can supplement existing assessments, providing carers and clinicians with a greater understanding of the causes and extent of agitation. Despite agitation frequently manifesting in repetitive hand movements, the automatic assessment of repetitive hand movements remains a sparsely researched field. Monitoring hand movements is problematic due to the subtle differences between different types of hand movements and variations in how they can be carried out; the lack of training data creates additional challenges. This paper proposes a novel approach to assess the type and intensity of repetitive hand movements using skeletal model data derived from video. We introduce a video-based dataset of five repetitive hand movements symptomatic of agitation. Using skeletal keypoint locations extracted from video, we demonstrate a system to recognise repetitive hand movements using discriminative poses. By first learning characteristics of the movement, our system can accurately identify changes in the intensity of repetitive movements. Wide inter-subject variation in agitated behaviours suggests the benefit of personalising the recognition model with some end-user information. Our results suggest that data captured using a single conventional RGB video camera can be used to automatically monitor agitated hand movements of sedentary patients
The TRECVID 2007 BBC rushes summarization evaluation pilot
This paper provides an overview of a pilot evaluation of
video summaries using rushes from several BBC dramatic series. It was carried out under the auspices of TRECVID.
Twenty-two research teams submitted video summaries of
up to 4% duration, of 42 individual rushes video files aimed
at compressing out redundant and insignificant material.
The output of two baseline systems built on straightforward
content reduction techniques was contributed by Carnegie
Mellon University as a control. Procedures for developing
ground truth lists of important segments from each video
were developed at Dublin City University and applied to
the BBC video. At NIST each summary was judged by
three humans with respect to how much of the ground truth
was included, how easy the summary was to understand,
and how much repeated material the summary contained.
Additional objective measures included: how long it took
the system to create the summary, how long it took the assessor to judge it against the ground truth, and what the
summary's duration was. Assessor agreement on finding desired segments averaged 78% and results indicate that while it is difficult to exceed the performance of baselines, a few systems did
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