749 research outputs found
A cell outage management framework for dense heterogeneous networks
In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
We present a novel procedural framework to generate an arbitrary number of
labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to
design accurate algorithms or training models for crowded scene understanding.
Our overall approach is composed of two components: a procedural simulation
framework for generating crowd movements and behaviors, and a procedural
rendering framework to generate different videos or images. Each video or image
is automatically labeled based on the environment, number of pedestrians,
density, behavior, flow, lighting conditions, viewpoint, noise, etc.
Furthermore, we can increase the realism by combining synthetically-generated
behaviors with real-world background videos. We demonstrate the benefits of
LCrowdV over prior lableled crowd datasets by improving the accuracy of
pedestrian detection and crowd behavior classification algorithms. LCrowdV
would be released on the WWW
Learning Behavioural Context
The original publication is available at www.springerlink.co
Automatic human behaviour anomaly detection in surveillance video
This thesis work focusses upon developing the capability to automatically evaluate
and detect anomalies in human behaviour from surveillance video. We work with
static monocular cameras in crowded urban surveillance scenarios, particularly air-
ports and commercial shopping areas. Typically a person is 100 to 200 pixels high
in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo-
ple at any given time. Our procedure evaluates human behaviour unobtrusively to
determine outlying behavioural events,
agging abnormal events to the operator.
In order to achieve automatic human behaviour anomaly detection we address
the challenge of interpreting behaviour within the context of the social and physical
environment. We develop and evaluate a process for measuring social connectivity
between individuals in a scene using motion and visual attention features. To do this
we use mutual information and Euclidean distance to build a social similarity matrix
which encodes the social connection strength between any two individuals. We de-
velop a second contextual basis which acts by segmenting a surveillance environment
into behaviourally homogeneous subregions which represent high tra c slow regions
and queuing areas. We model the heterogeneous scene in homogeneous subgroups
using both contextual elements. We bring the social contextual information, the
scene context, the motion, and visual attention features together to demonstrate
a novel human behaviour anomaly detection process which nds outlier behaviour
from a short sequence of video. The method, Nearest Neighbour Ranked Outlier
Clusters (NN-RCO), is based upon modelling behaviour as a time independent se-
quence of behaviour events, can be trained in advance or set upon a single sequence.
We nd that in a crowded scene the application of Mutual Information-based social
context permits the ability to prevent self-justifying groups and propagate anomalies
in a social network, granting a greater anomaly detection capability. Scene context
uniformly improves the detection of anomalies in all the datasets we test upon.
We additionally demonstrate that our work is applicable to other data domains.
We demonstrate upon the Automatic Identi cation Signal data in the maritime
domain. Our work is capable of identifying abnormal shipping behaviour using joint
motion dependency as analogous for social connectivity, and similarly segmenting
the shipping environment into homogeneous regions
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