87,069 research outputs found
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
We present a novel unsupervised deep learning framework for anomalous event
detection in complex video scenes. While most existing works merely use
hand-crafted appearance and motion features, we propose Appearance and Motion
DeepNet (AMDN) which utilizes deep neural networks to automatically learn
feature representations. To exploit the complementary information of both
appearance and motion patterns, we introduce a novel double fusion framework,
combining both the benefits of traditional early fusion and late fusion
strategies. Specifically, stacked denoising autoencoders are proposed to
separately learn both appearance and motion features as well as a joint
representation (early fusion). Based on the learned representations, multiple
one-class SVM models are used to predict the anomaly scores of each input,
which are then integrated with a late fusion strategy for final anomaly
detection. We evaluate the proposed method on two publicly available video
surveillance datasets, showing competitive performance with respect to state of
the art approaches.Comment: Oral paper in BMVC 201
Online real-time crowd behavior detection in video sequences
Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach
A Mimetic Strategy to Engage Voluntary Physical Activity In Interactive Entertainment
We describe the design and implementation of a vision based interactive
entertainment system that makes use of both involuntary and voluntary control
paradigms. Unintentional input to the system from a potential viewer is used to
drive attention-getting output and encourage the transition to voluntary
interactive behaviour. The iMime system consists of a character animation
engine based on the interaction metaphor of a mime performer that simulates
non-verbal communication strategies, without spoken dialogue, to capture and
hold the attention of a viewer. The system was developed in the context of a
project studying care of dementia sufferers. Care for a dementia sufferer can
place unreasonable demands on the time and attentional resources of their
caregivers or family members. Our study contributes to the eventual development
of a system aimed at providing relief to dementia caregivers, while at the same
time serving as a source of pleasant interactive entertainment for viewers. The
work reported here is also aimed at a more general study of the design of
interactive entertainment systems involving a mixture of voluntary and
involuntary control.Comment: 6 pages, 7 figures, ECAG08 worksho
Teaching humanoid robotics by means of human teleoperation through RGB-D sensors
This paper presents a graduate course project on humanoid robotics offered by the University of Padova. The target is to safely lift an object by teleoperating a small humanoid. Students have to map human limbs into robot joints, guarantee the robot stability during the motion, and teleoperate the robot to perform the correct movement. We introduce the following innovative aspects with respect to classical robotic classes: i) the use of humanoid robots as teaching tools; ii) the simplification of the stable locomotion problem by exploiting the potential of teleoperation; iii) the adoption of a Project-Based Learning constructivist approach as teaching methodology. The learning objectives of both course and project are introduced and compared with the students\u2019 background. Design and constraints students have to deal with are reported, together with the amount of time they and their instructors dedicated to solve tasks. A set of evaluation results are provided in order to validate the authors\u2019 purpose, including the students\u2019 personal feedback. A discussion about possible future improvements is reported, hoping to encourage further spread of educational robotics in schools at all levels
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