10,308 research outputs found
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
Video Time: Properties, Encoders and Evaluation
Time-aware encoding of frame sequences in a video is a fundamental problem in
video understanding. While many attempted to model time in videos, an explicit
study on quantifying video time is missing. To fill this lacuna, we aim to
evaluate video time explicitly. We describe three properties of video time,
namely a) temporal asymmetry, b)temporal continuity and c) temporal causality.
Based on each we formulate a task able to quantify the associated property.
This allows assessing the effectiveness of modern video encoders, like C3D and
LSTM, in their ability to model time. Our analysis provides insights about
existing encoders while also leading us to propose a new video time encoder,
which is better suited for the video time recognition tasks than C3D and LSTM.
We believe the proposed meta-analysis can provide a reasonable baseline to
assess video time encoders on equal grounds on a set of temporal-aware tasks.Comment: 14 pages, BMVC 201
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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