505,402 research outputs found
Group context learning for event recognition
We address the problem of group-level event recognition from videos. The events of interest are defined based on the motion and interaction of members in a group over time. Example events include group formation, dispersion, fol-lowing, chasing, flanking, and fighting. To recognize these complex group events, we propose a novel approach that learns the group-level scenario context from automatically extracted individual trajectories. We first perform a group structure analysis to produce a weighted graph that repre-sents the probabilistic group membership of the individuals. We then extract features from this graph to capture the mo-tion and action contexts among the groups. The features are represented using the “bag-of-words ” scheme. Finally, our method uses the learned Support Vector Machine (SVM) to classify a video segment into the six event categories. Our implementation builds upon a mature multi-camera multi-target tracking system that recognizes the group-level events involving up to 20 individuals in real-time. 1
Learning Behavioural Context
The original publication is available at www.springerlink.co
Reflective learning workshop
This is a collection of materials from an ESCalate workshop on reflective learning held at Coventry University, which covered: Developing an understanding of reflection; - Defining reflective learning; - Emotion and reflection; - Learning and reflection; - Factors that affect the quality of reflection; - Depth of reflection; - The learners conception of knowledge; -Introducing reflection, a two stage approach; - Assessment issues; - Exercises to stimulate reflection
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Sound events often occur in unstructured environments where they exhibit wide
variations in their frequency content and temporal structure. Convolutional
neural networks (CNN) are able to extract higher level features that are
invariant to local spectral and temporal variations. Recurrent neural networks
(RNNs) are powerful in learning the longer term temporal context in the audio
signals. CNNs and RNNs as classifiers have recently shown improved performances
over established methods in various sound recognition tasks. We combine these
two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it
on a polyphonic sound event detection task. We compare the performance of the
proposed CRNN method with CNN, RNN, and other established methods, and observe
a considerable improvement for four different datasets consisting of everyday
sound events.Comment: Accepted for IEEE Transactions on Audio, Speech and Language
Processing, Special Issue on Sound Scene and Event Analysi
Surveying human habit modeling and mining techniques in smart spaces
A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field
Detecting events and key actors in multi-person videos
Multi-person event recognition is a challenging task, often with many people
active in the scene but only a small subset contributing to an actual event. In
this paper, we propose a model which learns to detect events in such videos
while automatically "attending" to the people responsible for the event. Our
model does not use explicit annotations regarding who or where those people are
during training and testing. In particular, we track people in videos and use a
recurrent neural network (RNN) to represent the track features. We learn
time-varying attention weights to combine these features at each time-instant.
The attended features are then processed using another RNN for event
detection/classification. Since most video datasets with multiple people are
restricted to a small number of videos, we also collected a new basketball
dataset comprising 257 basketball games with 14K event annotations
corresponding to 11 event classes. Our model outperforms state-of-the-art
methods for both event classification and detection on this new dataset.
Additionally, we show that the attention mechanism is able to consistently
localize the relevant players.Comment: Accepted for publication in CVPR'1
Resources for Reflective Learning
This is a collection of exercises, material for handouts and examples that are designed to support the introduction and use of reflective and experiential learning. - It covers topics such as the processes of writing reflectively, questions to support reflective writing and strategies for enhancing learning from everyday experience
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