Opportunistic sensing for inferring in-the-wild human contexts based on activity pattern recognition using smart computing

2020 ◽  
Vol 106 ◽  
pp. 374-392 ◽  
Author(s):  
Muhammad Ehatisham-ul-Haq ◽  
Muhammad Awais Azam
Keyword(s):  
Pattern Recognition ◽  
Activity Pattern ◽  
Opportunistic Sensing ◽  
In The Wild ◽  
Smart Computing ◽  
Activity Pattern Recognition

Resident activity pattern recognition and comparison of six Sino-American metropolises

2019 ◽  
Vol 13 (3) ◽  
pp. 443-452
Author(s):  
Chao Yang ◽  
Wen Ye ◽  
Rongrong Zhu ◽  
Tianran Zhang
Keyword(s):  
Pattern Recognition ◽  
Activity Pattern ◽  
Activity Pattern Recognition

scholarly journals Comparative Analysis of Supervised and Unsupervised Approaches Applied to Large-Scale “In The Wild” Face Verification

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1832
Author(s):  
Tomasz Hachaj ◽  
Patryk Mazurek
Keyword(s):  
Pattern Recognition ◽  
Large Scale ◽  
Statistical Significance ◽  
Ground Truth ◽  
Classification Algorithm ◽  
Adjusted Rand Index ◽  
Face Verification ◽  
Data Set ◽  
In The Wild ◽  
Unsupervised Approaches

Deep learning-based feature extraction methods and transfer learning have become common approaches in the field of pattern recognition. Deep convolutional neural networks trained using tripled-based loss functions allow for the generation of face embeddings, which can be directly applied to face verification and clustering. Knowledge about the ground truth of face identities might improve the effectiveness of the final classification algorithm; however, it is also possible to use ground truth clusters previously discovered using an unsupervised approach. The aim of this paper is to evaluate the potential improvement of classification results of state-of-the-art supervised classification methods trained with and without ground truth knowledge. In this study, we use two sufficiently large data sets containing more than 200,000 “taken in the wild” images, each with various resolutions, visual quality, and face poses which, in our opinion, guarantee the statistical significance of the results. We examine several clustering and supervised pattern recognition algorithms and find that knowledge about the ground truth has a very small influence on the Fowlkes–Mallows score (FMS) of the classification algorithm. In the case of the classification algorithm that obtained the highest accuracy in our experiment, the FMS improved by only 5.3% (from 0.749 to 0.791) in the first data set and by 6.6% (from 0.652 to 0.718) in the second data set. Our results show that, beside highly secure systems in which face verification is a key component, face identities discovered by unsupervised approaches can be safely used for training supervised classifiers. We also found that the Silhouette Coefficient (SC) of unsupervised clustering is positively correlated with the Adjusted Rand Index, V-measure score, and Fowlkes–Mallows score and, so, we can use the SC as an indicator of clustering performance when the ground truth of face identities is not known. All of these conclusions are important findings for large-scale face verification problems. The reason for this is the fact that skipping the verification of people’s identities before supervised training saves a lot of time and resources.

Redefining the activity pattern of the honey possum (Tarsipes rostratus).

2001 ◽  
Vol 23 (2) ◽  
pp. 169 ◽  
Author(s):  
C Arrese ◽  
PB Runham
Keyword(s):  
Activity Pattern ◽  
Activity Patterns ◽  
Diurnal Activity ◽  
Numerous Species ◽  
Daytime Activity ◽  
In Captivity ◽  
In The Wild ◽  
Australian Marsupial ◽  
Diurnal Species ◽  
Honey Possum

ANIMALS are commonly separated into two major categories based on their activity patterns: diurnal and nocturnal. However, evidence of numerous species exhibiting diverse periods of activity, including arhythmic and crepuscular habits, broadens the description. The honey possum (Tarsipes rostratus), a small West-Australian marsupial feeding exclusively on nectar and pollen, has been described as strongly nocturnal (Wooller et al. 1981; Russell and Renfree 1989). However, infrequent daytime activity in captivity (Russell 1986) and in the wild under cold, cloudy conditions, has been reported (Hopper and Burbidge 1982; du Plessis and du Plessis 1995). During trapping exercises in the region of Jurien Bay (250 km north of Perth, Western Australia), several animals were observed foraging after sunrise and before sunset, with occasional diurnal activity. To date, no study has investigated directly the activity periods of the species. Furthermore, studies of the visual capabilities of T. rostratus revealed that its retinal organisation is not compatible with a nocturnal lifestyle, but presents features comparable to those found in diurnal species (Arrese 2002; Arrese et al. 2002). Such discrepancies warranted the monitoring of activity periods (rhythmicity) of T. rostratus in its natural environment, a study reported here. We discuss our results in the context of the visual ecology of the species.

scholarly journals Higher-level pattern features provide additional information to birds when recognizing and rejecting parasitic eggs

2019 ◽  
Vol 374 (1769) ◽  
pp. 20180197 ◽  
Author(s):  
Mary Caswell Stoddard ◽  
Benedict G. Hogan ◽  
Martin Stevens ◽  
Claire N. Spottiswoode
Keyword(s):  
Pattern Recognition ◽  
Visual Processing ◽  
Feature Detection ◽  
Individual Recognition ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Low Level ◽  
Additional Information ◽  
In The Wild ◽  
Egg Mimicry

Despite a recent explosion of research on pattern recognition, in both neuroscience and computer vision, we lack a basic understanding of how most animals perceive and respond to patterns in the wild. Avian brood parasites and their hosts provide an ideal study system for investigating the mechanisms of pattern recognition. The cuckoo finch, Anomalospiza imberbis , and its host the tawny-flanked prinia, Prinia subflava , lay highly polymorphic eggs with a great deal of variation in colour and patterning, with the cuckoo finch capable of close egg mimicry. Behavioural experiments in Zambia have previously shown that prinias use colour and multiple ‘low-level’ (occurring in early stages of visual processing) pattern attributes, derived from spatial frequency analysis, when rejecting foreign eggs. Here, we explore the extent to which host birds might also use ‘higher-level’ pattern attributes, derived from a feature detection algorithm, to make rejection decisions. Using a SIFT-based pattern recognition algorithm, N ature P attern M atch , we show that hosts are more likely to reject a foreign egg if its higher-level pattern features—which capture information about the shape and orientation of markings—differ from those of the host eggs. A revised statistical model explains about 37% variance in egg rejection behaviour, and differences in colour, low-level and higher-level pattern features all predict rejection, accounting for 42, 44 and 14% of the explained variance, respectively. Thus, higher-level pattern features provide a small but measurable improvement to the original model and may be especially useful when colour and low-level pattern features provide hosts with little information. Understanding the relative importance of low- and higher-level pattern features is a valuable goal for future work on animal coloration, especially in the contexts of mimicry, camouflage and individual recognition. This article is part of the theme issue ‘The coevolutionary biology of brood parasitism: from mechanism to pattern’.

scholarly journals Flexibility in a Single Behavioral Variable of Drosophila

2001 ◽  
Vol 8 (1) ◽  
pp. 1-10
Author(s):  
Martin Heisenberg ◽  
Reinhard Wolf ◽  
Björn Brembs
Keyword(s):  
Pattern Recognition ◽  
Flight Control ◽  
Visual Memory ◽  
High Speed ◽  
Visual Orientation ◽  
Orientation Behavior ◽  
Proboscis Extension Reflex ◽  
Behavioral Variable ◽  
In The Wild ◽  
Proboscis Extension

The flexibility of behavior is so rich, and its components are so exquisitely interwoven, that one may be well advised to turn to an isolated behavioral module for study. Gill withdrawal inAplysia, the proboscis extension reflex in the honeybee, and lid closure in mammals are such examples. We have chosen yawing, a single component of flight orientation in Drosophila melanogaster, for this approach. A specialty of this preparation is that the behavioral output can be reduced beyond the single module by one further step. It can be studied in tethered animals in which all turns are blocked while the differentially beating wings still provide the momentum. These intended yaw turns are measured by a torque meter to which the fly is hooked. The fly is held horizontally as if cruising at high speed. The head is glued to the thorax. It can bend its abdomen, extend its proboscis, and move its legs but cannot shift its direction of gaze or its orientation in space. Evidently, a fly hardly ever encounters this bizarre situation in the wild. We describe here the flexibility in this single behavioral variable. It provides insights into the relation between classical and operant conditioning, the processing of and interactions between the conditioned visual stimuli, early visual memory, visual pattern recognition, selective attention, and several other experience-dependent properties of visual orientation behavior. We start with a brief summary of visual flight control at the torque meter.

Sign in / Sign up

Export Citation Format

Share Document