scholarly journals Mashona Mole-Rat Automatic Individual Identification Based on the Mating Calls

2017 ◽  
Author(s):  
Veronika Dvorakova ◽  
Ladislav Ptacek ◽  
Ema Hrouzkova ◽  
Ludek Muller ◽  
Radim Sumbera
Keyword(s):  
Automatic System ◽  
Individual Recognition ◽  
Identification Accuracy ◽  
Individual Identification ◽  
Distinctive Features ◽  
Mole Rat ◽  
Vocal Signals ◽  
Mating Calls ◽  
Vocal Recognition

AbstractIn this study was tested mole-rat vocalization for presence of diverse individually distinctive features. An automatic system based on the GMM-UBM was used for individual recognition. The system distinguishes the recordings of the five mole-rats females. The overall achieved identification accuracy is 76.7%, the lowest 59.2%, and the highest 83.5%. The overall percentage is thus high enough to prove that the mating calls of the Mashona mole-rat can carry information about mole-rat individuality. Our results showed that studied vocal signals in the Mashona mole-rats are individually specific which indicates the possibility of individual vocal recognition in this species.

scholarly journals Cow Rump Identification Based on Lightweight Convolutional Neural Networks

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 361
Author(s):  
Handan Hou ◽  
Wei Shi ◽  
Jinyan Guo ◽  
Zhe Zhang ◽  
Weizheng Shen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Dairy Cow ◽  
Individual Recognition ◽  
Animal Husbandry ◽  
Identification Accuracy ◽  
Individual Identification ◽  
Production Environment ◽  
Accurate Identification ◽  
Computing Device

Individual identification of dairy cows based on computer vision technology shows strong performance and practicality. Accurate identification of each dairy cow is the prerequisite of artificial intelligence technology applied in smart animal husbandry. While the rump of each dairy cow also has lots of important features, so do the back and head, which are also important for individual recognition. In this paper, we propose a non-contact cow rump identification method based on convolutional neural networks. First, the rump image sequences of the cows while feeding were collected. Then, an object detection model was applied to detect the cow rump object in each frame of image. Finally, a fine-tuned convolutional neural network model was trained to identify cow rumps. An image dataset containing 195 different cows was created to validate the proposed method. The method achieved an identification accuracy of 99.76%, which showed a better performance compared to other related methods and a good potential in the actual production environment of cow husbandry, and the model is light enough to be deployed in an edge-computing device.

Individually distinctive pup vocalizations fail to prevent allo-suckling in grey seals

1999 ◽  
Vol 77 (5) ◽  
pp. 716-723 ◽  
Author(s):  
Susanne McCulloch ◽  
Patrick P Pomeroy ◽  
Peter JB Slater
Keyword(s):  
Breeding Season ◽  
Individual Recognition ◽  
Mutual Recognition ◽  
Halichoerus Grypus ◽  
Grey Seal ◽  
Playback Experiments ◽  
Auditory Cues ◽  
Distinctive Features ◽  
Vocal Recognition

In crowded aggregations that occur in breeding colonies, female pinnipeds commonly become separated from their pups and may use spatial, olfactory, or auditory cues to locate them. A system of mutual recognition based on vocalizations is known for otariids. Female phocids are known to use location and olfaction to help identify pups, but evidence for vocal recognition is weak. During the 1997 breeding season on the Isle of May, Scotland, vocalizations were recorded from grey seal, Halichoerus grypus, pups; playback experiments were carried out; and nursing of nonfilial pups was observed. Pup vocalizations were found to be both stereotyped and individually distinctive, features normally associated with a system of individual recognition. However, playback experiments revealed that mothers did not respond more to vocalizations of their own pups than to those of nonfilial pups. Furthermore, seventeen cases of allo-suckling were observed during 68 h of observation on the colony. High densities of animals and frequent separations present challenges to identification of pups by their mothers.

Acoustic Structure and Individual Variation of Grey Seal (Halichoerus Grypus) Pup Calls

1998 ◽  
Vol 78 (2) ◽  
pp. 651-658 ◽  
Author(s):  
Abigail K. Caudron ◽  
Andrei A. Kondakov ◽  
Serguei V. Siryanov
Keyword(s):  
Selective Pressure ◽  
Individual Recognition ◽  
Signal Strength ◽  
Halichoerus Grypus ◽  
Acoustic Features ◽  
Acoustic Structure ◽  
Vocal Signals ◽  
Vocal Recognition ◽  
Acoustic Variation ◽  
Maximum Signal

Throughout lactation, pups of pinnipeds regularly vocalize, including during interactions with their mother. In all studied species of otariids and in some phocids, these calls exhibit sufficient acoustic variation for individual recognition. As female grey seals Halichoerus grypus (Pinnipedia: Phocidae) often pup in dense colonies, and regularly leave their offspring alone to go to sea, the selective pressure for mutual vocal recognition between mothers and pups could be high. To investigate call variation in pups of this species, 170 calls of ten individual pups were analysed. Out of seven acoustic features studied, location of maximum signal strength, the number of harmonics and the frequency and strength of the fundamental show the highest individuality. However, the apparent infrequent use of pup calls during mother-pup reunions suggests that vocal signals may not play the major role in pup discrimination by female grey seals, as observed in other phocids which females do not vocalize to their pup.

scholarly journals Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sougata Sadhukhan ◽  
Holly Root-Gutteridge ◽  
Bilal Habib
Keyword(s):  
Reproductive Behaviour ◽  
Identification Accuracy ◽  
Individual Identification ◽  
Survey Method ◽  
Reliable Estimate ◽  
Mark Recapture ◽  
Test Dataset ◽  
Individual Level ◽  
Non Invasive ◽  
Supervised Classification Methods

AbstractPrevious studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. However, decreasing the bias in population estimations, such as by using Capture–Mark–Recapture, requires the identification of individuals using supervised classification methods, especially for sparsely populated species like the wolf which may otherwise be counted repeatedly. The cryptic behaviour of Indian wolf (Canis lupus pallipes) poses serious challenges to survey efforts, and thus, there is no reliable estimate of their population despite a prominent role in the ecosystem. Like other wolves, Indian wolves produce howls that can be detected over distances of more than 6 km, making them ideal candidates for acoustic surveys. Here, we explore the use of a supervised classifier to identify unknown individuals. We trained a supervised Agglomerative Nesting hierarchical clustering (AGNES) model using 49 howls from five Indian wolves and achieved 98% individual identification accuracy. We tested our model’s predictive power using 20 novel howls from a further four individuals (test dataset) and resulted in 75% accuracy in classifying howls to individuals. The model can reduce bias in population estimations using Capture-Mark-Recapture and track individual wolves non-invasively by their howls. This has potential for studies of wolves’ territory use, pack composition, and reproductive behaviour. Our method can potentially be adapted for other species with individually distinctive vocalisations, representing an advanced tool for individual-level monitoring.

Spot on: using camera traps to individually monitor one of the world’s largest lizards

2020 ◽  
Vol 47 (4) ◽  
pp. 326 ◽  
Author(s):  
Harry A. Moore ◽  
Jacob L. Champney ◽  
Judy A. Dunlop ◽  
Leonie E. Valentine ◽  
Dale G. Nimmo
Keyword(s):  
Activity Patterns ◽  
Image Data ◽  
Dorsal Surface ◽  
Camera Trap ◽  
Camera Traps ◽  
Identification Accuracy ◽  
Individual Identification ◽  
Lizard Species ◽  
Late Afternoon ◽  
Large Animals

Abstract ContextEstimating animal abundance often relies on being able to identify individuals; however, this can be challenging, especially when applied to large animals that are difficult to trap and handle. Camera traps have provided a non-invasive alternative by using natural markings to individually identify animals within image data. Although camera traps have been used to individually identify mammals, they are yet to be widely applied to other taxa, such as reptiles. AimsWe assessed the capacity of camera traps to provide images that allow for individual identification of the world’s fourth-largest lizard species, the perentie (Varanus giganteus), and demonstrate other basic morphological and behavioural data that can be gleaned from camera-trap images. MethodsVertically orientated cameras were deployed at 115 sites across a 10000km2 area in north-western Australia for an average of 216 days. We used spot patterning located on the dorsal surface of perenties to identify individuals from camera-trap imagery, with the assistance of freely available spot ID software. We also measured snout-to-vent length (SVL) by using image-analysis software, and collected image time-stamp data to analyse temporal activity patterns. ResultsNinety-two individuals were identified, and individuals were recorded moving distances of up to 1975m. Confidence in identification accuracy was generally high (91%), and estimated SVL measurements varied by an average of 6.7% (min=1.8%, max=21.3%) of individual SVL averages. Larger perenties (SVL of >45cm) were detected mostly between dawn and noon, and in the late afternoon and early evening, whereas small perenties (SVL of <30cm) were rarely recorded in the evening. ConclusionsCamera traps can be used to individually identify large reptiles with unique markings, and can also provide data on movement, morphology and temporal activity. Accounting for uneven substrates under cameras could improve the accuracy of morphological estimates. Given that camera traps struggle to detect small, nocturnal reptiles, further research is required to examine whether cameras miss smaller individuals in the late afternoon and evening. ImplicationsCamera traps are increasingly being used to monitor reptile species. The ability to individually identify animals provides another tool for herpetological research worldwide.

Acoustic cues to individual identity in the rattle calls of common blackbirds: a potential for individual recognition through multi-syllabic vocalisations emitted in both territorial and alarm contexts

Behaviour ◽  
2014 ◽  
Vol 152 (1) ◽  
pp. 57-82 ◽  
Author(s):  
Charline Couchoux ◽  
Torben Dabelsteen
Keyword(s):  
Individual Recognition ◽  
Dual Function ◽  
Individual Identity ◽  
Free Living ◽  
Bird Calls ◽  
Vocal Signals ◽  
The Common ◽  
The Individual ◽  
Types Of Information ◽  
Common Blackbird

Vocal signals convey many types of information, and individually recognizable cues can benefit signallers and receivers, as shown in birdsongs that are used in the contexts of mating and territoriality. Bird calls are typically less complex than songs and thus are likely to convey less information. However, the rattle calls of some species serve a dual function, being emitted as an anti-predator and deterrence signal, and thus may encode information on individual identity. We investigated these questions in the common blackbird (Turdus merula), which emits complex rattle calls in both territorial and alarm contexts. The vocalisations of free-living males were elicited and recorded by playing back songs of unknown males in birds’ territories (territorial context) and also while approaching individuals (predator context). These song-like highly-structured multi-syllabic calls typically had three types of elements. Acoustic and statistical analyses revealed, through elevated repeatability indexes, that most of the acoustic measurements used to describe the complexity of the calls (structural, temporal and frequency parameters) were highly variable, due to inter-individual differences. The size of the call and the characteristics of the starting element only were able to discriminate a high portion of the individual calls. Beyond the very well studied songs of oscines, calls therefore deserve more attention as they also carry a potential for conveying information on individual identity.

scholarly journals Image recognition of individual cow based on SIFT in Lαβ color space

2018 ◽  
Vol 176 ◽  
pp. 01023
Author(s):  
Feng Lv ◽  
Chunmei ZHANG ◽  
Changwei Lv
Keyword(s):  
Image Recognition ◽  
Individual Recognition ◽  
Low Cost ◽  
Color Space ◽  
Strong Stability ◽  
Identification Accuracy ◽  
Scale Invariant ◽  
Biological Features ◽  
Electronic Tag ◽  
Scale Invariant Feature

Using image recognition technology to identify individual dairy cattle with her biological features shows strong stability. This kind of non-contact, high precision and low cost individual recognition methods based on image processing are more and more popular recently to replace the electronic tag and ear mark which can hurt the cattle’s psychology and physical health and can affect cattle’s behavior. By comparing the various color space transformations, he proposed a scale-invariant feature transform algorithm based on the Luminace of Lαβ color space. With this algorithm, a biological features recognition and management system of Holstein cow has been developed. The identification accuracy is higher than 98%, which is the best result than all the similar reports for cows’ identification.

scholarly journals Sexual and individual signatures are encoded in the temporal rate of Cape gannet display calls

2021 ◽  
Author(s):  
Kezia Bowmaker-Falconer ◽  
Andrea Thiebault ◽  
Maelle Connan ◽  
Thierry Aubin ◽  
Isabelle Charrier ◽  
...  
Keyword(s):  
Sex Determination ◽  
Mate Selection ◽  
Animal Communication ◽  
Individual Recognition ◽  
Mate Recognition ◽  
Important Variable ◽  
Vital Role ◽  
Individual Identification ◽  
Seabird Species ◽  
Temporal Rate

Vocalisations play a vital role in animal communication, as they are involved in many biological functions. Seabirds often breed in large and dense colonies, making successful recognition between mates or between parents-and offspring crucial for reproductive success. Most seabird species, including Cape gannets (Morus capensis), are monomorphic and likely rely on acoustic signals for mate selection and mate recognition. This study aimed to better understand the use of vocalisations for sex and individual recognition in Cape gannets by describing the acoustic structure of their display calls at the nest. Vocalisations of nesting Cape gannets were recorded and acoustic measurements were extracted in both temporal and frequency domains. Values of the fundamental frequency and the average of Inter-Onset-Interval appeared to be the most important acoustic variables for sex determination. Both temporal and frequency parameters showed a potential for individual identity coding, with the average units Inter-Onset-Interval being the most important variable for individual identification for both sexes. This study provides the first evidence of sex-specific and individual vocal signatures in adult breeding Cape gannets. From an applied perspective, identified sex specific differences could potentially be used as a non-invasive method for field-based sex-determination in research and monitoring projects on Cape gannets.

scholarly journals Radar Emitter Individual Identification Based on Convolutional Neural Network Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wei Sun ◽  
Lihua Wang ◽  
Songlin Sun
Keyword(s):  
Identification Accuracy ◽  
Individual Identification ◽  
Identification Performance ◽  
Neural Network Learning ◽  
Hilbert Huang Transform ◽  
Network Learning ◽  
Radar Systems ◽  
Mode Decomposition ◽  
Signal Image ◽  
Real Time Identification

Radar Emitter Individual Identification is a key technology in modern electronic radar systems. This paper will focus on Radar Emitter Individual Identification (REII). Based on the advantages of Empirical Mode Decomposition (EMD) and bispectrum in signal processing, we propose an REII method based on the CNN. Firstly, the radar emitter signal is preprocessed. Secondly, the Hilbert–Huang Transform (HHT) spectrum and bispectrum are combined to form an image of the signal. Finally, in order to avoid loss of information and achieve the potential identification performance improvement, the signal image obtained is identified by the optimized CNN. Experimental results based on the measured signals show that the proposed method has high identification accuracy and is capable of meeting real-time identification requirements. The deep-learning-based identification method proposed in this paper has strong generalization ability and adaptability, which provides a new way for REII.

scholarly journals Deep learning-based methods for individual recognition in small birds

2019 ◽  
Author(s):  
André C. Ferreira ◽  
Liliana R. Silva ◽  
Francesco Renna ◽  
Hanja B. Brandl ◽  
Julien P. Renoult ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Collection ◽  
Individual Recognition ◽  
Bird Species ◽  
Training Data ◽  
Individual Identification ◽  
List Type ◽  
Major Advance ◽  
General Applicability ◽  
Automated Method

ABSTRACTIndividual identification is a crucial step to answer many questions in evolutionary biology and is mostly performed by marking animals with tags. Such methods are well established but often make data collection and analyses time consuming and consequently are not suited for collecting very large datasets.Recent technological and analytical advances, such as deep learning, can help overcome these limitations by automatizing data collection and analysis. Currently one of the bottlenecks preventing the application of deep learning for individual identification is the need of hundreds to thousands of labelled pictures required for training convolutional neural networks (CNNs).Here, we describe procedures that improve data collection and allow individual identification in captive and wild birds and we apply it to three small bird species, the sociable weaver Philetairus socius, the great tit Parus major and the zebra finch Taeniopygia guttata.First, we present an automated method that allows the collection of large samples of individually labelled images. Second, we describe how to train a CNN to identify individuals. Third, we illustrate the general applicability of CNN for individual identification in animal studies by showing that the trained CNN can predict the identity of birds from images collected in contexts that differ from the ones originally used to train the CNNs. Fourth, we present a potential solution to solve the issues of new incoming individuals.Overall our work demonstrates the feasibility of applying state-of-the-art deep learning tools for individual identification of birds, both in the lab and in the wild. These techniques are made possible by our approaches that allow efficient collection of training data. The ability to conduct individual identification of birds without requiring external markers that can be visually identified by human observers represents a major advance over current methods.

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