16 research outputs found
A Novel Approach in Analyzing Traffic Flow by Extreme Learning Machine Method
The objective of this study is to detect abnormal behaviours of moving objects captured in highway traffic flow footages, classify them by using artificial learning methods, and lastly to predict the future thereof (regression). To this end, the system being the object of the design and application consists of three stages. In the first stage, to detect the moving object in the video, background/foreground segmentation method of Mixture of Gaussian (MOG), and to track the moving object, Kalman Filter-Hungarian algorithm method have been used. In the second stage, by using the coordinates of the object, such details as location, distance in terms of time, and speed of the object are obtained, and by using total pixel count data relating to the shape of the object are obtained. The software based on the specifically elaborated algorithm compares these data with the data in the table of rules set down for the road under surveillance, and generates an attribute table comprising anomalies of the objects in the video. In the last stage, however, the data included in the attribute table have been classified and predictions by the artificial learning method, Extreme Learning Machine (ELM) made
Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set
Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments
Spatio-temporal Video Parsing for Abnormality Detection
Abnormality detection in video poses particular challenges due to the
infinite size of the class of all irregular objects and behaviors. Thus no (or
by far not enough) abnormal training samples are available and we need to find
abnormalities in test data without actually knowing what they are.
Nevertheless, the prevailing concept of the field is to directly search for
individual abnormal local patches or image regions independent of another. To
address this problem, we propose a method for joint detection of abnormalities
in videos by spatio-temporal video parsing. The goal of video parsing is to
find a set of indispensable normal spatio-temporal object hypotheses that
jointly explain all the foreground of a video, while, at the same time, being
supported by normal training samples. Consequently, we avoid a direct detection
of abnormalities and discover them indirectly as those hypotheses which are
needed for covering the foreground without finding an explanation for
themselves by normal samples. Abnormalities are localized by MAP inference in a
graphical model and we solve it efficiently by formulating it as a convex
optimization problem. We experimentally evaluate our approach on several
challenging benchmark sets, improving over the state-of-the-art on all standard
benchmarks both in terms of abnormality classification and localization.Comment: 15 pages, 12 figures, 3 table
AnoML-IoT: An End to End Re-configurable Multi-protocol Anomaly Detection Pipeline for Internet of Things
The rapid development in ubiquitous computing has enabled the use of
microcontrollers as edge devices. These devices are used to develop truly
distributed IoT-based mechanisms where machine learning (ML) models are
utilized. However, integrating ML models to edge devices requires an
understanding of various software tools such as programming languages and
domain-specific knowledge. Anomaly detection is one of the domains where a high
level of expertise is required to achieve promising results. In this work, we
present AnoML which is an end-to-end data science pipeline that allows the
integration of multiple wireless communication protocols, anomaly detection
algorithms, deployment to the edge, fog, and cloud platforms with minimal user
interaction. We facilitate the development of IoT anomaly detection mechanisms
by reducing the barriers that are formed due to the heterogeneity of an IoT
environment. The proposed pipeline supports four main phases: (i) data
ingestion, (ii) model training, (iii) model deployment, (iv) inference and
maintaining. We evaluate the pipeline with two anomaly detection datasets while
comparing the efficiency of several machine learning algorithms within
different nodes. We also provide the source code
(https://gitlab.com/IOTGarage/anoml-iot-analytics) of the developed tools which
are the main components of the pipeline.Comment: Elsevier Internet of Things, Volume 16, 100437, December 202
AnoML-IoT: an end to end re-configurable multi-protocol anomaly detection pipeline for Internet of Things
The rapid development in ubiquitous computing has enabled the use of microcontrollers as edge devices. These devices are used to develop truly distributed IoT-based mechanisms where machine learning (ML) models are utilized. However, integrating ML models to edge devices requires an understanding of various software tools such as programming languages and domain-specific knowledge. Anomaly detection is one of the domains where a high level of expertise is required to achieve promising results. In this work, we present AnoML which is an end-to-end data science pipeline that allows the integration of multiple wireless communication protocols, anomaly detection algorithms, deployment to the edge, fog, and cloud platforms with minimal user interaction. We facilitate the development of IoT anomaly detection mechanisms by reducing the barriers that are formed due to the heterogeneity of an IoT environment. The proposed pipeline supports four main phases: (i) data ingestion, (ii) model training, (iii) model deployment, (iv) inference and maintaining. We evaluate the pipeline with two anomaly detection datasets while comparing the efficiency of several machine learning algorithms within different nodes. We also provide the source code of the developed tools which are the main components of the pipeline