CNN-GRU Based Hybrid Architecture for Video Classification of Birds

Sudharsan K , Gowtham M , Vanitha Ravi

Bird classification plays a critical role in ornithology and wildlife conservation. With the rise of video data, the demand for efficient and accurate video-based bird classification methods has increased. This research introduces a hybrid architecture combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) for bird video classification. The CNN extracts spatial features from video frames, while the GRU models the temporal dependencies. The combination of global and local feature extraction enables the model to capture both fine-grained and coarse-grained bird behaviors, resulting in improved classification performance.

Published Paper   E-Certificate

"CNN-GRU Based Hybrid Architecture for Video Classification of Birds", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 10, page no.a723-a726, October-2024, Available :https://ijnrd.org/papers/IJNRD2410075.pdf

Volume 9 Issue 10, October-2024

Pages : a723-a726

Paper Reg. ID: IJNRD_301152

Published Paper Id: IJNRD2410075

Downloads: 00050

Research Area: Science and Technology

Country: Pudukkottai, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2410075

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2410075

DOI: http://doi.one/10.1729/Journal.41856

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition