89,683 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
Developing a robust framework to reduce the size of a recorded video surveillance systems
Most of the video surveillance strategies take a significant amount of space for storage
as surveillance camera's unexceptionally recorded everything during camera – on time.
Whereby, it leads to consuming the storage capacity of the device of the system. In fact,
many algorithms have been proposed solving in the dilemma to object recognition and
compress the video to reduce the size whenever it save's data. Nevertheless, the
technology deprived efficient methods to reducing the storage of space for consummation.
The Idea of this paper is to propose a framework on how to possibly can be reduce the
size of a recorded video of the surveillance system via recording only the part of the video
that contains the motion, and ignore the other parts based on the motion detection. The
result shows that the framework give an outstanding results on the uncompressed
surveillance video recorded from a single fixed camera. The proposed framework enables
to save 30% more of playback time and can provide more than 50% of storage of space
saving
An intelligent surveillance platform for large metropolitan areas with dense sensor deployment
Producción CientíficaThis paper presents an intelligent surveillance platform based on the usage of
large numbers of inexpensive sensors designed and developed inside the European Eureka
Celtic project HuSIMS. With the aim of maximizing the number of deployable units while
keeping monetary and resource/bandwidth costs at a minimum, the surveillance platform is
based on the usage of inexpensive visual sensors which apply efficient motion detection
and tracking algorithms to transform the video signal in a set of motion parameters. In
order to automate the analysis of the myriad of data streams generated by the visual
sensors, the platform’s control center includes an alarm detection engine which comprises
three components applying three different Artificial Intelligence strategies in parallel.
These strategies are generic, domain-independent approaches which are able to operate in
several domains (traffic surveillance, vandalism prevention, perimeter security, etc.). The
architecture is completed with a versatile communication network which facilitates data
collection from the visual sensors and alarm and video stream distribution towards the
emergency teams. The resulting surveillance system is extremely suitable for its
deployment in metropolitan areas, smart cities, and large facilities, mainly because cheap
visual sensors and autonomous alarm detection facilitate dense sensor network deployments
for wide and detailed coveraMinisterio de Industria, Turismo y Comercio and the Fondo de Desarrollo Regional (FEDER) and the Israeli Chief Scientist Research Grant 43660 inside the European Eureka Celtic project HuSIMS (TSI-020400-2010-102)
Optimal Control of MDPs with Temporal Logic Constraints
In this paper, we focus on formal synthesis of control policies for finite
Markov decision processes with non-negative real-valued costs. We develop an
algorithm to automatically generate a policy that guarantees the satisfaction
of a correctness specification expressed as a formula of Linear Temporal Logic,
while at the same time minimizing the expected average cost between two
consecutive satisfactions of a desired property. The existing solutions to this
problem are sub-optimal. By leveraging ideas from automata-based model checking
and game theory, we provide an optimal solution. We demonstrate the approach on
an illustrative example.Comment: Technical report accompanying the CDC 2013 pape
Quadrotor control for persistent surveillance of dynamic environments
Thesis (M.S.)--Boston UniversityThe last decade has witnessed many advances in the field of small scale unmanned aerial vehicles (UAVs). In particular, the quadrotor has attracted significant attention. Due to its ability to perform vertical takeoff and landing, and to operate in cluttered spaces, the quadrotor is utilized in numerous practical applications, such as reconnaissance and information gathering in unsafe or otherwise unreachable environments.
This work considers the application of aerial surveillance over a city-like environment. The thesis presents a framework for automatic deployment of quadrotors to monitor and react to dynamically changing events. The framework has a hierarchical structure. At the top level, the UAVs perform complex behaviors that satisfy high- level mission specifications. At the bottom level, low-level controllers drive actuators on vehicles to perform the desired maneuvers.
In parallel with the development of controllers, this work covers the implementation of the system into an experimental testbed. The testbed emulates a city using physical objects to represent static features and projectors to display dynamic events occurring on the ground as seen by an aerial vehicle. The experimental platform features a motion capture system that provides position data for UAVs and physical features of the environment, allowing for precise, closed-loop control of the vehicles. Experimental runs in the testbed are used to validate the effectiveness of the developed control strategies
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