WildWatch AI
Problem Statement
Human-wildlife conflicts are increasingly prevalent in areas where
urban expansion intersects with natural habitats, posing
significant risks to both people and wildlife. These conflicts often
result in dangerous encounters between residents and predators
such as leopards, leading to potential harm to individuals and
retaliatory actions against animals.
Existing monitoring systems, including camera traps, infrared
imaging, and public alert mechanisms, have limitations that
exacerbate these issues. A faster, user-friendly and cheap system
utilizing computer vision and automation is necessary.
Solution
The goal of our project is to develop a real-time leopard
detection and notification alert system which will help
folks in rural areas and communities around national parks
tp avoid any dangerous conflicts with leopards.
Building a machine learning model that will be trained
upon 400+ manually annotated images of leopards in
various different background and lighting conditions for
better results when detecting leopards in real-life
scenarios.
Competitive Analysis
Wildlife Tracking System Market size was valued at USD 15.4
Billion in 2023 and is projected to reach USD 42.8 Billion by
2031, growing at a CAGR of 14.3% during the forecast period
2024-2031.
Focusing on AI integration, the AI in wildlife conservation
monitoring market was valued at $1.8 billion in 2023 and is
projected to reach $16.5 billion by 2032, growing at a CAGR
of 28.4% from 2024 to 2032.
by VERIFIED MARKET RESEARCH
and ALLIED MARKET RESEARCH
Total Addressable Market
Key Partnerships Key Activities Value Propositions
Research Institutions Research & Development Real-Time Monitoring
Government Agencies Deployment Public Safety
NGOs and Conservation Maintenance Conservation Impact
Groups Community Training Scalability
Technology Companies Marketing & Awareness Cost Efficiency
Local Communities
Customer Relationships Customer Segmentation Key Resources
Local government bodies Technology
Real-Time Interaction
NGOs Human Resources
Training Programs
National parks Data Resources
Technical Support
Agricultural communities Partnership Networks
Cost Structure Revenue Streams
Product Sales
Fixed Costs: R&D investments, Equipment and Hardware
Subscription Services
for deployment
Collaboration and Partnerships
Variable Costs: Maintenance and Upgrades, Data Storage
Maintenance Contracts
Operational Costs: Training programs for users
Plan for Scaling-up
In the future, the system could be expanded to detect and monitor
additional wildlife species, providing a more comprehensive solution
for various types of human-wildlife conflicts.
Integrating advanced features such as predictive analytics could allow
the system to anticipate wildlife movements and proactively alert
communities, further enhancing safety measures.
Expanding the system's integration with other conservation
technologies and smart city infrastructures could improve its
effectiveness and scalability.
By collaborating with global wildlife conservation initiatives,
"WildWatch AI" could contribute to more effective management and
preservation efforts worldwide, making it a versatile and valuable tool in
wildlife conservation and human safety.
Team Composition
Project Name: WildWatch AI
College Name: Vasantdada Patil Pratishthan’s College
of Engineering & Visual Arts
Team Members:
Sanil Jadhav B.E. Computer Engineering
Siddhesh Patil B.E. Computer Engineering
Shubhankar Kadam B.E. Computer Engineering
Mayuresh Patil B.E. Computer Engineering
