Pain Detection Through Automated Video Analysis

Author(s):  
2014 ◽  
Author(s):  
Heather Twaddle ◽  
Tobias Schendzielorz ◽  
Oliver Fakler ◽  
Sasan Amini

2014 ◽  
Vol 160 ◽  
pp. 132-137 ◽  
Author(s):  
S. Ott ◽  
C.P.H. Moons ◽  
M.A. Kashiha ◽  
C. Bahr ◽  
F.A.M. Tuyttens ◽  
...  

1996 ◽  
Vol 20 (6) ◽  
pp. 1037-1049 ◽  
Author(s):  
Toshikazu Shinba ◽  
Ken-Ichi Yamamoto ◽  
Gong-Min Cao ◽  
G.O. Mugishima ◽  
Yoshinori Andow ◽  
...  

Marine Policy ◽  
2020 ◽  
Vol 116 ◽  
pp. 103785
Author(s):  
Julien Simon ◽  
Dorothée Kopp ◽  
Pascal Larnaud ◽  
Jean-Philippe Vacherot ◽  
Fabien Morandeau ◽  
...  

2013 ◽  
Vol 243 ◽  
pp. 306-312 ◽  
Author(s):  
E.B. Adamah-Biassi ◽  
I. Stepien ◽  
R.L. Hudson ◽  
M.L. Dubocovich

Author(s):  
Wayne Strasser

Previously published efforts regarding the unsteady nature of a self-exciting air/water (AW) coaxial three-stream airblast injector considered first the effects of feed stream flow rate changes and then the effects of nozzle geometric permutations. Frequency domain analysis, automated video analysis, and spray profile assessments were used to draw conclusions about spray quality and character. The computational method was validated using an AW test stand (AWTS). Here, the focus of the work shifts to the use of slurry and a high-density gas (SH). Again, the effects of flow rate and nozzle geometry are considered. It was found that the nature of the SH flow changed dramatically in comparison with its AW counterpart. As a result, the video analysis technique had to be revamped, and therefore direct comparisons are limited. As with its AW counterpart, inner nozzle retraction and stream meeting angle proved to be the most influential geometry variables. A flushed nozzle showed a wider spray with a strongly trimodal character. Increasing the relative inner gas flow rate had a pronounced, but mixed, effect on the considered metrics. In general, the transient signatures of the pressure and video analysis metrics were similar enough to indicate that the unsteady driving mechanisms were consistent for each. Lastly, attempts to further stimulate the spray via modulating the inner gas proved futile for both sets of flowing materials for the measures considered.


2021 ◽  
Vol 11 (17) ◽  
pp. 8141
Author(s):  
Vladimir Kulyukin ◽  
Nikhil Ganta ◽  
Anastasiia Tkachenko

Omnidirectional honeybee traffic is the number of bees moving in arbitrary directions in close proximity to the landing pad of a beehive over a period of time. Automated video analysis of such traffic is critical for continuous colony health assessment. In our previous research, we proposed a two-tier algorithm to measure omnidirectional bee traffic in videos. Our algorithm combines motion detection with image classification: in tier 1, motion detection functions as class-agnostic object location to generate regions with possible objects; in tier 2, each region from tier 1 is classified by a class-specific classifier. In this article, we present an empirical and theoretical comparison of random reinforced forests and shallow convolutional networks as tier 2 classifiers. A random reinforced forest is a random forest trained on a dataset with reinforcement learning. We present several methods of training random reinforced forests and compare their performance with shallow convolutional networks on seven image datasets. We develop a theoretical framework to assess the complexity of image classification by a image classifier. We formulate and prove three theorems on finding optimal random reinforced forests. Our conclusion is that, despite their limitations, random reinforced forests are a reasonable alternative to convolutional networks when memory footprints and classification and energy efficiencies are important factors. We outline several ways in which the performance of random reinforced forests may be improved.


2020 ◽  
Vol 47 (8) ◽  
pp. 982-997
Author(s):  
Mohamed H. Zaki ◽  
Tarek Sayed ◽  
Moataz Billeh

Video-based traffic analysis is a leading technology for streamlining transportation data collection. With traffic records from video cameras, unsupervised automated video analysis can detect various vehicle measures such as vehicle spatial coordinates and subsequently lane positions, speed, and other dynamic measures without the need of any physical interconnections to the road infrastructure. This paper contributes to the unsupervised automated video analysis by addressing two main shortcomings of the approach. The first objective is to alleviate tracking problems of over-segmentation and over-grouping by integrating region-based detection with feature-based tracking. This information, when combined with spatiotemporal constraints of grouping, can reduce the effects of these problems. This fusion approach offers a superior decision procedure for grouping objects and discriminating between trajectories of objects. The second objective is to model three-dimensional bounding boxes for the vehicles, leading to a better estimate of their geometry and consequently accurate measures of their position and travel information. This improvement leads to more precise measurement of traffic parameters such as average speed, gap time, and headway. The paper describes the various steps of the proposed improvements. It evaluates the effectiveness of the refinement process on data collected from traffic cameras in three different locations in Canada and validates the results with ground truth data. It illustrates the effectiveness of the improved unsupervised automated video analysis with a case study on 10 h of traffic data collection such as volume and headway measurements.


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