scholarly journals Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2848 ◽  
Author(s):  
Leonel Rosas-Arias ◽  
Jose Portillo-Portillo ◽  
Aldo Hernandez-Suarez ◽  
Jesus Olivares-Mercado ◽  
Gabriel Sanchez-Perez ◽  
...  
Keyword(s):  
Principal Component ◽  
Subspace Learning ◽  
Transport Systems ◽  
Video Sequences ◽  
Projected Image ◽  
The Road ◽  
Illumination Changes ◽  
On Line ◽  
Incremental Learning Method ◽  
On Line Learning

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average—dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.

scholarly journals On-line Learning Algorithms for extracting respiratory activity from Single Lead ECGs based on Principal Component Analysis

2012 ◽  
Vol 57 (SI-1 Track-L) ◽  
Author(s):  
Maik Pflugradt ◽  
Steffen Mann ◽  
Viktor Feller ◽  
Reinhold Orglmeister
Keyword(s):  
Principal Component Analysis ◽  
Respiratory Activity ◽  
Learning Algorithms ◽  
Principal Component ◽  
Component Analysis ◽  
On Line ◽  
On Line Learning

A Self-Organizing Fuzzy Controller for an Active Suspension System

2003 ◽  
Vol 9 (9) ◽  
pp. 1023-1040 ◽  
Author(s):  
Shiuh-Jer Huang ◽  
Wei-Cheng Lin
Keyword(s):  
Ride Comfort ◽  
Fuzzy Controller ◽  
Dead Zone ◽  
Suspension System ◽  
Learning Ability ◽  
The Road ◽  
On Line ◽  
Divergence Problem ◽  
On Line Learning ◽  
Self Organizing

A quarter-car hydraulic suspension system has been constructed to evaluate the performance of active vehicle suspension. Since this hydraulic actuating suspension system has a nonlinear and complicated mathematical model, it is difficult to design a model-based controller. Hence, a self-organizing fuzzy controller (SOFC) is employed to control the position and acceleration oscillation amplitudes of the sprung mass due to the rough road variation. This approach has learning ability for responding to the time-varying characteristic of the oscillation coming from the tire. Its control rule bank can be established and modified continuously by on-line learning. E-modification and dead-zone concepts are introduced into the SOFC fuzzy adaptation rule to improve the oscillation feature of control law and the gradual divergence problem. The experimental results show that this intelligent controller effectively suppresses the vibration amplitude and reduces the acceleration of the sprung mass correlating to the road variation for improving the vehicle ride comfort.

Patterns of Implicit Learning Below the Level of Conscious Knowledge

2010 ◽  
Vol 24 (2) ◽  
pp. 91-101 ◽  
Author(s):  
Juliana Yordanova ◽  
Rolf Verleger ◽  
Ullrich Wagner ◽  
Vasil Kolev
Keyword(s):  
Implicit Learning ◽  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Knowledge Generation ◽  
Sleep Stages ◽  
Implicit Processing ◽  
Implicit Processes ◽  
Late Night ◽  
On Line ◽  
On Line Learning

The objective of the present study was to evaluate patterns of implicit processing in a task where the acquisition of explicit and implicit knowledge occurs simultaneously. The number reduction task (NRT) was used as having two levels of organization, overt and covert, where the covert level of processing is associated with implicit associative and implicit procedural learning. One aim was to compare these two types of implicit processes in the NRT when sleep was or was not introduced between initial formation of task representations and subsequent NRT processing. To assess the effects of different sleep stages, two sleep groups (early- and late-night groups) were used where initial training of the task was separated from subsequent retest by 3 h full of predominantly slow wave sleep (SWS) or rapid eye movement (REM) sleep. In two no-sleep groups, no interval was introduced between initial and subsequent NRT performance. A second aim was to evaluate the interaction between procedural and associative implicit learning in the NRT. Implicit associative learning was measured by the difference between the speed of responses that could or could not be predicted by the covert abstract regularity of the task. Implicit procedural on-line learning was measured by the practice-based increased speed of performance with time on task. Major results indicated that late-night sleep produced a substantial facilitation of implicit associations without modifying individual ability for explicit knowledge generation or for procedural on-line learning. This was evidenced by the higher rate of subjects who gained implicit knowledge of abstract task structure in the late-night group relative to the early-night and no-sleep groups. Independently of sleep, gain of implicit associative knowledge was accompanied by a relative slowing of responses to unpredictable items suggesting reciprocal interactions between associative and motor procedural processes within the implicit system. These observations provide evidence for the separability and interactions of different patterns of processing within implicit memory.

Mental Models of On-line Learning: An Interview Study of University Decision-Makers

2008 ◽  
Author(s):  
Monica Guzman ◽  
Eric Amsel
Keyword(s):  
Mental Models ◽  
Interview Study ◽  
Decision Makers ◽  
On Line ◽  
On Line Learning

On line learning for Personal and Professional Development in Medicine

MedEdPublish ◽  
2015 ◽  
Author(s):  
Annemarie Hennessy ◽  
Brad Frankum
Keyword(s):  
Professional Development ◽  
On Line ◽  
On Line Learning

Implementing On-Line Learning and Performance Support Using an EPSS

10.28945/1520 ◽  
2011 ◽  
Vol 7 ◽  
pp. 213-224 ◽  
Author(s):  
Eran Gal ◽  
Rafi Nachmias
Keyword(s):  
Performance Support ◽  
On Line ◽  
And Performance ◽  
On Line Learning ◽  
Learning And Performance

Generic monitoring tools for water characteristics assessment

2003 ◽  
Vol 3 (1-2) ◽  
pp. 351-357
Author(s):  
S. Le Bonté ◽  
M.-N. Pons ◽  
O. Potier ◽  
S. Chanel ◽  
M. Baklouti
Keyword(s):  
Municipal Wastewater ◽  
Principal Component ◽  
Toxic Substances ◽  
Capacity Curves ◽  
Water Characteristics ◽  
Monitoring Tools ◽  
Visible Spectra ◽  
On Line ◽  
Mining Procedure

An adaptive principal component analysis applied to sets of data provided by global analytical methods (UV-visible spectra, buffer capacity curves, respirometric tests) is proposed as a generic procedure for on-line and fast characterization of wastewater. The data-mining procedure is able to deal with a large amount of information, takes into account the normal variations of wastewater composition related to human activity, and enables a rapid detection of abnormal situations such as the presence of toxic substances by comparison of the actual wastewater state with a continuously updated reference. The procedure has been validated on municipal wastewater.

SCROLLA — On-Line Learning and Assessment

2001 ◽  
Vol 1 (2) ◽  
pp. 27-27
Author(s):  
Cliff Beevers
Keyword(s):  
On Line ◽  
On Line Learning
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