Recognition and classification of animals based on texture features through parallel computing

Manohar, N; Subrahmanya, S.; Bharathi, R.; Kumar, Y. H. Sharath; G, Hemantha Kumar

doi:10.1109/ccip.2016.7802872

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…Instead of randomly observing, here we propose to observe the biological species based on the taxonomy which is the science of defining biological species based on shared characteristics. The list of species could be categorized according to their specific abilities or nature (Huxley, 1875; Shane et al, 1986; Santori et al, 2005; Alexander, 2006; Manohar et al, 2016) as follows: Based on locomotion modes (e.g., crawling, swimming, flying, and jumping).Based on appearances (e.g., size and color).Based on living conditions and mediums (e.g., terrestrial, arboreal, and aquatic).Based on scientific classifications (e.g., kingdom, class, and family).Based on social organization patterns (e.g., solitary and social).…”

Section: System-of-systems Bio-inspired Engineering Design Processmentioning

confidence: 99%

A System-of-Systems Bio-Inspired Design Process: Conceptual Design and Physical Prototype of a Reconfigurable Robot Capable of Multi-Modal Locomotion

et al. 2019

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Modern engineering problems require solutions with multiple functionalities in order to meet their practical needs to handle a variety of applications in different scenarios. Conventional design paradigms for single design purpose may not be able to satisfy this requirement efficiently. This paper proposes a novel system-of-systems bio-inspired design method framed in a solution-driven bio-inspired design paradigm. The whole design process consists of eight steps, that is, (1) biological solutions identification, (2) biological solutions definition/champion biological solutions, (3) principle extraction from each champion biological solution, (4) merging of extracted principles, (5) solution reframing, (6) problem search, (7) problem definition, and (8) principles application & implementation. The steps are elaborated and a case study of reconfigurable robots is presented following these eight steps. The design originates from the multimodal locomotion capabilities of two species (i.e., spiders and primates) and is analyzed based on the Pugh analysis. The resulting robotic platform could be potentially used for urban patrolling purposes.

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Section: System-of-systems Bio-inspired Engineering Design Processmentioning

confidence: 99%

A System-of-Systems Bio-Inspired Design Process: Conceptual Design and Physical Prototype of a Reconfigurable Robot Capable of Multi-Modal Locomotion

et al. 2019

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“…Despite a deviation in the distribution of herd activity, the collective activities of the herd were accurately identified. Manohar et al [7] proposed an effective system for animal recognition and classification based on texture features, which obtained the required features from the local appearance and texture of the animals. Furthermore, they classified them by using K nearest neighbor (KNN) and support vector machine.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Wildlife Recognition Model Based on Multi-Branch Aggregation and Squeeze-And-Excitation Network

Xie

Zhang

et al. 2019

Applied Sciences

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Infrared camera trapping, which helps capture large volumes of wildlife images, is a widely-used, non-intrusive monitoring method in wildlife surveillance. This method can greatly reduce the workload of zoologists through automatic image identification. To achieve higher accuracy in wildlife recognition, the integrated model based on multi-branch aggregation and Squeeze-and-Excitation network is introduced. This model adopts multi-branch aggregation transformation to extract features, and uses Squeeze-and-Excitation block to adaptively recalibrate channel-wise feature responses based on explicit self-mapped interdependencies between channels. The efficacy of the integrated model is tested on two datasets: the Snapshot Serengeti dataset and our own dataset. From experimental results on the Snapshot Serengeti dataset, the integrated model applies to the recognition of 26 wildlife species, with the highest accuracies in Top-1 (when the correct class is the most probable class) and Top-5 (when the correct class is within the five most probable classes) at 95.3% and 98.8%, respectively. Compared with the ROI-CNN algorithm and ResNet (Deep Residual Network), on our own dataset, the integrated model, shows a maximum improvement of 4.4% in recognition accuracy.

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Analytical Study of Animal Biometrics: A Technical Survey

Kumar¹,

Singh

et al. 2017

Animal Biometrics

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Recognition and classification of animals based on texture features through parallel computing

Cited by 9 publications

References 6 publications

A System-of-Systems Bio-Inspired Design Process: Conceptual Design and Physical Prototype of a Reconfigurable Robot Capable of Multi-Modal Locomotion

A System-of-Systems Bio-Inspired Design Process: Conceptual Design and Physical Prototype of a Reconfigurable Robot Capable of Multi-Modal Locomotion

An Integrated Wildlife Recognition Model Based on Multi-Branch Aggregation and Squeeze-And-Excitation Network

Analytical Study of Animal Biometrics: A Technical Survey

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