Human–Elephant Conflict (HEC) has become a major socio-environmental issue in India and other Asian countries due to deforestation, agricultural expansion, infrastructure development, and increasing human presence in elephant habitats. These conflicts often lead to crop damage, loss of human lives, property destruction, and harm to elephants. Existing mitigation methods such as manual patrolling, physical barriers, and basic sensor systems are mostly reactive and fail to provide early warnings. Recent advances in computer vision enable automated wildlife monitoring through camera traps and surveillance systems, but most approaches focus only on detecting elephant presence rather than analyzing behavioral patterns over time. This paper proposes a vision-based elephant posture and behavior recognition system for early HEC mitigation by combining YOLOv8 for real-time posture detection with a Long Short-Term Memory (LSTM) network for temporal behavior classification. The system extracts posture features such as ear expansion, trunk lifting, and body inclination from video frames, while motion features including bounding box movement, velocity, and posture transitions are analyzed across sequences to classify behaviors into calm, alert, warning, aggressive, and HEC-risk states. By separating posture recognition from behavior prediction, the proposed system enables intent-focused wildlife monitoring and can provide early warnings to forest officials and nearby communities. The approach is scalable, non-invasive, and suitable for deployment near forest boundaries, agricultural regions, and elephant corridors to reduce Human-Elephant Conflict.
Varma et al. (2026) studied this question.