76 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
Modeling Dynamic Swarms
This paper proposes the problem of modeling video sequences of dynamic swarms
(DS). We define DS as a large layout of stochastically repetitive spatial
configurations of dynamic objects (swarm elements) whose motions exhibit local
spatiotemporal interdependency and stationarity, i.e., the motions are similar
in any small spatiotemporal neighborhood. Examples of DS abound in nature,
e.g., herds of animals and flocks of birds. To capture the local spatiotemporal
properties of the DS, we present a probabilistic model that learns both the
spatial layout of swarm elements and their joint dynamics that are modeled as
linear transformations. To this end, a spatiotemporal neighborhood is
associated with each swarm element, in which local stationarity is enforced
both spatially and temporally. We assume that the prior on the swarm dynamics
is distributed according to an MRF in both space and time. Embedding this model
in a MAP framework, we iterate between learning the spatial layout of the swarm
and its dynamics. We learn the swarm transformations using ICM, which iterates
between estimating these transformations and updating their distribution in the
spatiotemporal neighborhoods. We demonstrate the validity of our method by
conducting experiments on real video sequences. Real sequences of birds, geese,
robot swarms, and pedestrians evaluate the applicability of our model to real
world data.Comment: 11 pages, 17 figures, conference paper, computer visio
Fitting Jump Models
We describe a new framework for fitting jump models to a sequence of data.
The key idea is to alternate between minimizing a loss function to fit multiple
model parameters, and minimizing a discrete loss function to determine which
set of model parameters is active at each data point. The framework is quite
general and encompasses popular classes of models, such as hidden Markov models
and piecewise affine models. The shape of the chosen loss functions to minimize
determine the shape of the resulting jump model.Comment: Accepted for publication in Automatic
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