18,042 research outputs found
Lost in Time: Temporal Analytics for Long-Term Video Surveillance
Video surveillance is a well researched area of study with substantial work
done in the aspects of object detection, tracking and behavior analysis. With
the abundance of video data captured over a long period of time, we can
understand patterns in human behavior and scene dynamics through data-driven
temporal analytics. In this work, we propose two schemes to perform descriptive
and predictive analytics on long-term video surveillance data. We generate
heatmap and footmap visualizations to describe spatially pooled trajectory
patterns with respect to time and location. We also present two approaches for
anomaly prediction at the day-level granularity: a trajectory-based statistical
approach, and a time-series based approach. Experimentation with one year data
from a single camera demonstrates the ability to uncover interesting insights
about the scene and to predict anomalies reasonably well.Comment: To Appear in Springer LNE
Multi-criteria Anomaly Detection using Pareto Depth Analysis
We consider the problem of identifying patterns in a data set that exhibit
anomalous behavior, often referred to as anomaly detection. In most anomaly
detection algorithms, the dissimilarity between data samples is calculated by a
single criterion, such as Euclidean distance. However, in many cases there may
not exist a single dissimilarity measure that captures all possible anomalous
patterns. In such a case, multiple criteria can be defined, and one can test
for anomalies by scalarizing the multiple criteria using a linear combination
of them. If the importance of the different criteria are not known in advance,
the algorithm may need to be executed multiple times with different choices of
weights in the linear combination. In this paper, we introduce a novel
non-parametric multi-criteria anomaly detection method using Pareto depth
analysis (PDA). PDA uses the concept of Pareto optimality to detect anomalies
under multiple criteria without having to run an algorithm multiple times with
different choices of weights. The proposed PDA approach scales linearly in the
number of criteria and is provably better than linear combinations of the
criteria.Comment: Removed an unnecessary line from Algorithm
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
Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
To plan safe trajectories in urban environments, autonomous vehicles must be
able to quickly assess the future intentions of dynamic agents. Pedestrians are
particularly challenging to model, as their motion patterns are often uncertain
and/or unknown a priori. This paper presents a novel changepoint detection and
clustering algorithm that, when coupled with offline unsupervised learning of a
Gaussian process mixture model (DPGP), enables quick detection of changes in
intent and online learning of motion patterns not seen in prior training data.
The resulting long-term movement predictions demonstrate improved accuracy
relative to offline learning alone, in terms of both intent and trajectory
prediction. By embedding these predictions within a chance-constrained motion
planner, trajectories which are probabilistically safe to pedestrian motions
can be identified in real-time. Hardware experiments demonstrate that this
approach can accurately predict pedestrian motion patterns from onboard
sensor/perception data and facilitate robust navigation within a dynamic
environment.Comment: Submitted to 2014 International Workshop on the Algorithmic
Foundations of Robotic
Unravelling intermittent features in single particle trajectories by a local convex hull method
We propose a new model-free method to detect change points between distinct
phases in a single random trajectory of an intermittent stochastic process. The
local convex hull (LCH) is constructed for each trajectory point, while its
geometric properties (e.g., the diameter or the volume) are used as
discriminators between phases. The efficiency of the LCH method is validated
for six models of intermittent motion, including Brownian motion with different
diffusivities or drifts, fractional Brownian motion with different Hurst
exponents, and surface-mediated diffusion. We discuss potential applications of
the method for detection of active and passive phases in the intracellular
transport, temporal trapping or binding of diffusing molecules, alternating
bulk and surface diffusion, run and tumble (or search) phases in the motion of
bacteria and foraging animals, and instantaneous firing rates in neurons
Fractional Fourier detection of L\'evy Flights: application to Hamiltonian chaotic trajectories
A signal processing method designed for the detection of linear (coherent)
behaviors among random fluctuations is presented. It is dedicated to the study
of data recorded from nonlinear physical systems. More precisely the method is
suited for signals having chaotic variations and sporadically appearing regular
linear patterns, possibly impaired by noise. We use time-frequency techniques
and the Fractional Fourier transform in order to make it robust and easily
implementable. The method is illustrated with an example of application: the
analysis of chaotic trajectories of advected passive particles. The signal has
a chaotic behavior and encounter L\'evy flights (straight lines). The method is
able to detect and quantify these ballistic transport regions, even in noisy
situations
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