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Perusal of Camera Trap Sequences Across Locations

  • Conference paper
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Pattern Recognition Applications and Methods (ICPRAM 2021, ICPRAM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13822))

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Abstract

The current rate of decline in biodiversity exclaims ecological conservation. In response, camera traps are being increasingly deployed for the perlustration of wildlife. The analyses of camera trap data can aid in curbing species extinction. However, a substantial amount of time is lost in the manual review curtailing the usage of camera traps for prompt decision-making. The insuperable visual challenges and proneness of camera trap to record empty frames (frames that are natural backdrops with no wildlife presence) deem wildlife detection and species recognition a demanding and taxing task. Thus, we propose a pipeline for wildlife detection and species recognition to expedite the processing of camera trap sequences. The proposed pipeline consists of three stages: (i) empty frame removal, (ii) wildlife detection, and (iii) species recognition and classification. We leverage vision transformer (ViT), DEtection TRansformer (DETR), vision and detection transformer (ViDT), faster region based convolutional neural network (Faster R-CNN), inception v3, and ResNet 50 for the same. We examine the adroitness of the leveraged algorithms at new and unseen locations against the challenges of domain generalisation. We demonstrate the effectiveness of the proposed pipeline using the Caltech camera trap (CCT) dataset.

This work is partially supported by the National Mission for Himalayan Studies (NMHS) grant GBPNI/NMHS-2019-20/SG/314.

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Authors and Affiliations

  1. MANAS Lab, SCEE, Indian Institute of Technology Mandi, Kamand, H.P, India

    Anoushka Banerjee, Dileep Aroor Dinesh & Arnav Bhavsar

Authors
  1. Anoushka Banerjee
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  2. Dileep Aroor Dinesh
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  3. Arnav Bhavsar
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Correspondence to Anoushka Banerjee .

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Editors and Affiliations

  1. Sapienza Università di Roma, Rome, Italy

    Maria De Marsico

  2. ICAR, Consiglio Nazionale delle Ricerche, Naples, Napoli, Italy

    Gabriella Sanniti di Baja

  3. IST - Torre Norte, Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

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Banerjee, A., Dinesh, D.A., Bhavsar, A. (2023). Perusal of Camera Trap Sequences Across Locations. In: De Marsico, M., Sanniti di Baja, G., Fred, A. (eds) Pattern Recognition Applications and Methods. ICPRAM ICPRAM 2021 2022. Lecture Notes in Computer Science, vol 13822. Springer, Cham. https://doi.org/10.1007/978-3-031-24538-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-24538-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24537-4

  • Online ISBN: 978-3-031-24538-1

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