Authors

Kalimisetty Yamuna Venkata Sai Sirisha

UG Student, Dept. of E.C.E, GATES Institute of Technology, Gooty, Ananthapur, Andhra Pradesh, India

Dr. Archek Praveen kumar

Professor, Dept of E.C.E, GATES Institute of Technology, Gooty, Ananthapur, Andhra Pradesh, India

Kummetha Vishnu Vardhan Reddy

UG Student, Dept. of E.C.E, GATES Institute of Technology, Gooty, Ananthapur, Andhra Pradesh, India

Mulinti Thirumala Reddy

UG Student, Dept. of E.C.E, GATES Institute of Technology, Gooty, Ananthapur, Andhra Pradesh, India

Chinnanarasappagari Sravani

UG Student, Dept. of E.C.E, GATES Institute of Technology, Gooty, Ananthapur, Andhra Pradesh, India

Neelakanti Vishnu Vardhan Reddy

UG Student, Dept. of E.C.E, GATES Institute of Technology, Gooty, Ananthapur, Andhra Pradesh, India

Abstract

Understanding the behaviour of wild animals is crucial for ecological research aimed at conserving biodiversity and gaining insights into their natural habitats. This paper presents a method for the automatic detection and classification of animal behaviour through deep learning techniques, enabling the analysis of animal activities from video recordings without human involvement. By integrating Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs), we developed a model capable of identifying and categorizing various patterns of animal behaviour—including running, feeding, and resting— among others. The model learns from the features present in video frames through rapid training using popular pretrained architectures such as Resnet and Inception, enhancing its accuracy. Additionally, we employed a Long Short-Term Memory (LSTM) network to capture the temporal dynamics of animal actions, allowing the model to identify patterns across multiple frames and understand how behaviours progress over time. The model was trained and evaluated on a substantial dataset of annotated video clips featuring wild animals in diverse habitats. The results demonstrate high accuracy in classifying animal activities, highlighting the potential of deep learning for creating automated, non-invasive wildlife monitoring solutions. This system represents a powerful tool for conservation, capable of processing and analysing vast amounts of video footage, ultimately providing valuable insights into animal behaviours and habitat utilization that are essential for developing more effective conservation strategies.

Keywords

Wild animal activity detection Deep neural networks Computer vision Machine learning Wildlife conservation Animal tracking Camera traps Image classification Object detection

Citation of this Article

Kalimisetty Yamuna Venkata Sai Sirisha, Dr. Archek Praveen kumar, Kummetha Vishnu Vardhan Reddy, Mulinti Thirumala Reddy, Chinnanarasappagari Sravani, & Neelakanti Vishnu Vardhan Reddy. (2025). Wild Animal Activity Detection Using Deep Neural Networks in Python. International Current Journal of Engineering and Science - ICJES, 4(3), 60-66. Article DOI: https://doi.org/10.47001/ICJES/2025.403007

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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