47,273 research outputs found
Future Frame Prediction for Anomaly Detection -- A New Baseline
Anomaly detection in videos refers to the identification of events that do
not conform to expected behavior. However, almost all existing methods tackle
the problem by minimizing the reconstruction errors of training data, which
cannot guarantee a larger reconstruction error for an abnormal event. In this
paper, we propose to tackle the anomaly detection problem within a video
prediction framework. To the best of our knowledge, this is the first work that
leverages the difference between a predicted future frame and its ground truth
to detect an abnormal event. To predict a future frame with higher quality for
normal events, other than the commonly used appearance (spatial) constraints on
intensity and gradient, we also introduce a motion (temporal) constraint in
video prediction by enforcing the optical flow between predicted frames and
ground truth frames to be consistent, and this is the first work that
introduces a temporal constraint into the video prediction task. Such spatial
and motion constraints facilitate the future frame prediction for normal
events, and consequently facilitate to identify those abnormal events that do
not conform the expectation. Extensive experiments on both a toy dataset and
some publicly available datasets validate the effectiveness of our method in
terms of robustness to the uncertainty in normal events and the sensitivity to
abnormal events.Comment: IEEE Conference on Computer Vision and Pattern Recognition 201
Learning Temporal Transformations From Time-Lapse Videos
Based on life-long observations of physical, chemical, and biologic phenomena
in the natural world, humans can often easily picture in their minds what an
object will look like in the future. But, what about computers? In this paper,
we learn computational models of object transformations from time-lapse videos.
In particular, we explore the use of generative models to create depictions of
objects at future times. These models explore several different prediction
tasks: generating a future state given a single depiction of an object,
generating a future state given two depictions of an object at different times,
and generating future states recursively in a recurrent framework. We provide
both qualitative and quantitative evaluations of the generated results, and
also conduct a human evaluation to compare variations of our models.Comment: ECCV201
Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks
Taking a photo outside, can we predict the immediate future, e.g., how would
the cloud move in the sky? We address this problem by presenting a generative
adversarial network (GAN) based two-stage approach to generating realistic
time-lapse videos of high resolution. Given the first frame, our model learns
to generate long-term future frames. The first stage generates videos of
realistic contents for each frame. The second stage refines the generated video
from the first stage by enforcing it to be closer to real videos with regard to
motion dynamics. To further encourage vivid motion in the final generated
video, Gram matrix is employed to model the motion more precisely. We build a
large scale time-lapse dataset, and test our approach on this new dataset.
Using our model, we are able to generate realistic videos of up to resolution for 32 frames. Quantitative and qualitative experiment results
have demonstrated the superiority of our model over the state-of-the-art
models.Comment: To appear in Proceedings of CVPR 201
Unsupervised Learning of Long-Term Motion Dynamics for Videos
We present an unsupervised representation learning approach that compactly
encodes the motion dependencies in videos. Given a pair of images from a video
clip, our framework learns to predict the long-term 3D motions. To reduce the
complexity of the learning framework, we propose to describe the motion as a
sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent
Neural Network based Encoder-Decoder framework to predict these sequences of
flows. We argue that in order for the decoder to reconstruct these sequences,
the encoder must learn a robust video representation that captures long-term
motion dependencies and spatial-temporal relations. We demonstrate the
effectiveness of our learned temporal representations on activity
classification across multiple modalities and datasets such as NTU RGB+D and
MSR Daily Activity 3D. Our framework is generic to any input modality, i.e.,
RGB, Depth, and RGB-D videos.Comment: CVPR 201
Class-Agnostic Counting
Nearly all existing counting methods are designed for a specific object
class. Our work, however, aims to create a counting model able to count any
class of object. To achieve this goal, we formulate counting as a matching
problem, enabling us to exploit the image self-similarity property that
naturally exists in object counting problems. We make the following three
contributions: first, a Generic Matching Network (GMN) architecture that can
potentially count any object in a class-agnostic manner; second, by
reformulating the counting problem as one of matching objects, we can take
advantage of the abundance of video data labeled for tracking, which contains
natural repetitions suitable for training a counting model. Such data enables
us to train the GMN. Third, to customize the GMN to different user
requirements, an adapter module is used to specialize the model with minimal
effort, i.e. using a few labeled examples, and adapting only a small fraction
of the trained parameters. This is a form of few-shot learning, which is
practical for domains where labels are limited due to requiring expert
knowledge (e.g. microbiology). We demonstrate the flexibility of our method on
a diverse set of existing counting benchmarks: specifically cells, cars, and
human crowds. The model achieves competitive performance on cell and crowd
counting datasets, and surpasses the state-of-the-art on the car dataset using
only three training images. When training on the entire dataset, the proposed
method outperforms all previous methods by a large margin.Comment: Asian Conference on Computer Vision (ACCV), 201
PredNet and Predictive Coding: A Critical Review
PredNet, a deep predictive coding network developed by Lotter et al.,
combines a biologically inspired architecture based on the propagation of
prediction error with self-supervised representation learning in video. While
the architecture has drawn a lot of attention and various extensions of the
model exist, there is a lack of a critical analysis. We fill in the gap by
evaluating PredNet both as an implementation of the predictive coding theory
and as a self-supervised video prediction model using a challenging video
action classification dataset. We design an extended model to test if
conditioning future frame predictions on the action class of the video improves
the model performance. We show that PredNet does not yet completely follow the
principles of predictive coding. The proposed top-down conditioning leads to a
performance gain on synthetic data, but does not scale up to the more complex
real-world action classification dataset. Our analysis is aimed at guiding
future research on similar architectures based on the predictive coding theory
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