Wild Animal Detection System — AI Wildlife Monitoring for Railways, Highways & Agriculture

A wild animal detection system is an AI-powered solution that uses cameras and computer vision to automatically detect and identify wild animals in real time, then triggers instant alerts to prevent collisions, crop damage, or human-wildlife conflict. Unlike motion sensors, it identifies the actual species — an elephant versus a stray dog versus a person — rather than just detecting movement. With thermal cameras it works day and night, and on edge devices it operates even in remote areas with poor connectivity.

Motion sensors trigger on any movement — wind, leaves, small animals — producing constant false alerts and never identifying what is there. Radar can detect an object at range but cannot tell an elephant from a vehicle, and it is expensive and complex to deploy. AI camera detection identifies the actual species, works at night with thermal imaging, produces video evidence for verification, and retrofits onto existing cameras — giving accurate, actionable alerts instead of noise.

Yes, and night detection is essential because most animal movement and most collisions happen after dark. Paired with thermal cameras, the system detects an animal's heat signature in complete darkness, fog, or smoke, and the AI classifies it before triggering an alert. It also supports IR night-vision IP cameras, with representative thermal detection ranges up to around 200 metres.

The system targets species central to Indian human-wildlife conflict — elephants, tigers, leopards, wild boar, nilgai, sambar and chital deer, sloth bear, monkeys, stray cattle, wild dogs, large birds for airports, and more. Custom species training is available for region-specific animals, typically within two to four weeks using your own camera feeds. Detection works day and night when paired with thermal cameras.

Yes. The AI runs on a local edge device, so detection and alerting continue with zero or intermittent connectivity, syncing to the Microsoft Azure India cloud only when a connection is available. Combined with solar-powered, rugged IP66/IP67 cameras and an optional satellite link, it forms a fully off-grid detection station. This is designed for exactly the remote railway, highway, forest, and plantation stretches where conflict is worst.

The AI processes each camera frame in under a second and fires an alert immediately on classification, so detection-to-alert happens in seconds. This is the same real-time pipeline behind Tentovision's sub-5-second fire detection in enterprise deployments at Nippon Paint. Speed matters because a loco pilot or driver may have only 10 to 15 seconds to react to an animal at night.

Yes — this is a core use case. Cameras at vulnerable crossing points detect approaching elephants, including at night with thermal imaging, and alert loco pilots, station masters, and forest officials so trains can slow in time, with optional integration into railway signalling scoped per project. It complements the Distributed Acoustic Sensing (DAS) systems Indian Railways are scaling on elephant corridors by adding visual confirmation and species identification, and it can be deployed at crossings and stations where trackside fibre is not laid.

Tentovision is vendor-agnostic and works over the open ONVIF and RTSP standards, so it supports Hikvision, Dahua, CP Plus, Axis, and other IP cameras, plus thermal and IR night-vision cameras. It runs on cameras you already have as well as new deployments, which avoids rip-and-replace costs. Thermal cameras are recommended wherever night detection is required.

Cost depends on the number of cameras, whether thermal units are needed, site connectivity, and deployment scale, so it is quoted per project. The Tentovision Edge Device starts at around ₹6,000 (indicative) — a fraction of sensor or radar alternatives — and working with existing cameras reduces cost further. Pricing is transparent and project-specific, and a pilot lets you validate value before committing to scale. See pricing for indicative ranges.

Yes, and it is the recommended approach. A pilot of 5 to 10 cameras on a single corridor section, farm boundary, or runway zone validates detection accuracy in your specific environment, lighting, and species mix before any large-scale rollout. The pilot produces real detection data and video evidence you can evaluate, and it scales seamlessly into full deployment on the same platform.