Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Detecting the movement direction of animals using
footprints has long held significance in wildlife monitoring and
ecological studies. This research introduces a novel system that
leverages artificial intelligence to enhance animal direction
detection through footprint analysis. By integrating advanced
image processing techniques with machine learning models, the
system can identify footprint patterns, infer orientation, and
predict movement direction across various terrains. The
approach minimizes human error, increases the speed and
accuracy of field data interpretation, and supports real-time
tracking in conservation and habitat mapping efforts. Results
demonstrate the system's capability to process diverse footprint
samples with high precision, offering a scalable tool for
researchers, park rangers, and environmental agencies. This AI-
powered method paves the way for automated ecological
surveillance and a deeper understanding of animal behavior.
"AI Powered Animal Direction Detection via Footprint Analysis", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.c592-c596, May-2026, Available :http://www.ijrti.org/papers/IJRTI2604343.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator