NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping

Caubet, Yves; Richard, Freddie-Jeanne

doi:10.3897/zookeys.515.9390

Cited by 4 publications

(5 citation statements)

References 36 publications

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“…Several marking techniques also exist to follow individuals, which can facilitate the monitoring of species during experiments (Hagler & Jackson, ), and such technical approaches provide a good working basis for further experimentation on mixed‐species groups. Moreover, various theories, mathematical models and metrics have been developed in the context of aggregation and could be applied or adapted to mixed‐species groups (for metrics, see Ives, ; Sauphanor & Sureau, ; Everaerts et al ., or Caubet & Richard, ; for models, see Deneubourg et al ., or Nicolis et al ., ). However, models of the cooperation–competition phenomenon still need to be established for mixed‐species groups, but the required experimental data are currently lacking.…”

Section: Resultsmentioning

confidence: 99%

“…In this context, Hassall et al . () also demonstrated that two species of woodlice, Porcellio scaber and Armadillidium vulgare , can clump together (see also Caubet & Richard, ). Consistent with the Allee effect, these authors found that at low densities, mixed‐species groups promote population growth that results in positive fitness consequences (higher growth rates and survivorship of group members) (Hassall et al ., ).…”

Section: Benefitsmentioning

confidence: 88%

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Mixed‐species aggregations in arthropods

et al. 2017

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This review offers the first synthesis of the research on mixed-species groupings of arthropods and highlights the behavioral and evolutionary questions raised by such behavior. Mixed-species groups are commonly found in mammals and birds. Such groups are also observed in a large range of arthropod taxa independent of their level of sociality. Several examples are presented to highlight the mechanisms underlying such groupings, particularly the evidence for phylogenetic proximity between members that promotes cross-species recognition. The advantages offered by such aggregates are described and discussed. These advantages can be attributed to the increase in group size and could be identical to those of nonmixed groupings, but competition-cooperation dynamics might also be involved, and such effects may differ between homo- and heterospecific groups. We discuss three extreme cases of interspecific recognition that are likely involved in mixed-species groups as vectors for cross-species aggregation: tolerance behavior between two social species, one-way mechanism in which one species is attractive to others and two-way mechanism of mutual attraction. As shown in this review, the study of mixed-species groups offers biologists an interesting way to explore the frontiers of cooperation-competition, including the process of sympatric speciation.

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Section: Resultsmentioning

confidence: 99%

Section: Benefitsmentioning

confidence: 88%

Mixed‐species aggregations in arthropods

et al. 2017

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“…Many studies devoted to heterospecific interactions have been reported, from unicellular and social amoeba 34 to woodlices 35 , ants 36 37 and mammals 2 20 38 . For example, some species of Triatominae are attracted to the chemical signals left from individuals of the same and of different species 39 .…”

Section: Discussionmentioning

confidence: 99%

Transition between segregation and aggregation: the role of environmental constraints

Nicolis

Halloy

Deneubourg

2016

Sci Rep

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Interactions between sub-groups (species, strains) have been reported in many species among many taxae. We propose a generic model based on earlier experiments accounting for both conspecific (or between individuals of the same strains) and heterospecific (or between strains) interactions. The model predicts different collective behaviours without any change of individuals’ algorithm as some key generic parameters such as the carrying capacity, the number of individuals involved and the strength of inter-attraction between sub-groups are varied. A key result is the possibility for sub-groups to segregate between patches and for transition between different patterns, even in absence of active agonistic behaviour. The model can be viewed as a network of feedbacks that is independent of the signals or cues involved in mixed groups interactions. Its predictions are therefore applicable to a wide spectrum of situations including social insects (inter castes interaction) and provides insights on possible mechanisms that can be at work.

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“…It has made remarkable achievements in the fields of transportation, medicine, communication, geology, and so on. This paper mainly studies the related content of image processing technology in the field of transportation, including the identification of moving objects in the traffic video image, the prediction of moving objects' driving track, and the application in the intelligent transportation system [20,21].…”

Section: Statistical Distribution Of Traffic Flowmentioning

confidence: 99%

The Theory and Method of Data Acquisition of Mixed Traffic Popular People and Nonmotor Vehicles Based on Image Processing

Wang

Yao

Zhao

et al. 2022

Mobile Information Systems

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In road mixed traffic, pedestrians and nonmotor vehicles have a great impact on the driving of motor vehicles. This kind of influence not only threatens the road traffic safety but also leads to the increase of delay and the decrease of traffic capacity. The purpose of this paper is to study the theory and method of data acquisition of mixed traffic popular people and nonmotor vehicles based on image processing technology. Aiming at the problem that the basic state space model solves the phenomenon of “failure” such as mutual interference between mixed objects, this paper proposes a KF tracking model based on a fuzzy matching method to realize the effective and accurate tracking of mixed traffic objects. The experimental results show that, after extracting the morphological features of the detected pedestrian and nonmotor vehicle images and using the method of pattern recognition to classify, recognize, and count the mixed traffic objects, through the comparison of the two trajectory lines, we can see that the tracking accuracy of the algorithm is high under the mutual interference of pedestrian and nonmotor vehicle. Excluding the detection error, the pedestrian tracking error is less than 10 pixels, the average error is 2.366 pixels, the maximum error of nonmotor vehicle tracking is 19 pixels, and the average error is 2.5 pixels.

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NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping

Cited by 4 publications

References 36 publications

Mixed‐species aggregations in arthropods

Mixed‐species aggregations in arthropods

Transition between segregation and aggregation: the role of environmental constraints

The Theory and Method of Data Acquisition of Mixed Traffic Popular People and Nonmotor Vehicles Based on Image Processing

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