Wildlife Sentinel: Physics-Informed AI for Autonomous Wildlife Conservation 🐘

Tagline: Transforming passive wildlife cameras into an intelligent conservation network using robust motion estimation, adaptive computer vision, and AI-assisted threat assessment.

Inspiration

Wildlife reserves span vast geographical areas, making continuous monitoring difficult for ranger teams. Camera traps and network-connected surveillance cameras can capture valuable footage, but detecting an animal is only the first step toward understanding what is happening.

A conventional object detector can identify an elephant, deer, or predator and estimate its location in an image. However, a sequence of detections does not automatically reveal whether an animal is running, resting, fleeing, or behaving abnormally.

During the development of Wildlife Sentinel, we identified several challenges associated with motion estimation from video:

Bounding-box instability: An animal bending its neck, moving its legs, or changing its posture can shift its bounding-box center even when its torso barely moves.
Variable processing latency: When frames are processed at irregular intervals, calculating velocity without accurately accounting for elapsed time produces misleading motion estimates.
Unstable movement direction: When an animal is stationary or moving very slowly, small detection errors can produce large fluctuations in its estimated heading.
Occlusion: When an animal disappears behind vegetation and reappears, its new detection may be incorrectly interpreted as an extreme acceleration or an entirely different animal.
Limited computational resources: Continuously running expensive vision models on every camera stream can increase processing costs and power consumption.

These challenges inspired Wildlife Sentinel.

Our goal is to combine computer vision with classical state estimation and intelligent event analysis to build a wildlife-monitoring system that understands motion more reliably, allocates computational resources according to scene activity, and helps conservation teams investigate potentially important events.

Instead of treating every detection as an isolated observation, Wildlife Sentinel maintains a continuously updated estimate of each tracked animal's movement.

What It Does

Wildlife Sentinel is a modular wildlife-monitoring system designed to transform camera footage into actionable information.

It combines object detection, segmentation, multi-object tracking, Kalman filtering, motion anomaly detection, and cloud-based event reporting.

1. Wildlife Detection and Tracking

The system receives video from an RTSP-compatible camera or a recorded wildlife video.

A YOLO-based vision model identifies animals in each processed frame. When segmentation is enabled, the model also estimates the visible animal's silhouette.

A multi-object tracker associates detections across successive frames and assigns persistent track identifiers where possible.

For each tracked animal, the system maintains information such as:

Detected species or animal category.
Track identifier.
Image-space position.
Estimated velocity and acceleration.
Movement direction when the estimated speed is sufficiently high.
Tracking confidence and uncertainty.
Recent movement history.

The resulting information provides the foundation for subsequent motion analysis.

2. Physics-Informed Motion Estimation

Raw object detections contain noise caused by changes in posture, segmentation boundaries, lighting, and partial occlusion.

Wildlife Sentinel uses a Kalman filter to combine new visual measurements with predictions based on the animal's previous motion.

The filter maintains an estimated state consisting of position and velocity:

$$ \mathbf{x}k = \begin{bmatrix} x_k & y_k & v{x,k} & v_{y,k} \end{bmatrix}^{T} $$

Here, (x_k) and (y_k) represent image coordinates, while (v_{x,k}) and (v_{y,k}) represent image-space velocity components.

For a variable frame interval (\Delta t), the constant-velocity transition model is:

\mathbf{x}_{k+1}

\mathbf{F}(\Delta t)\mathbf{x}_k + \mathbf{w}_k $$

where

$$ \mathbf{F}(\Delta t)= \begin{bmatrix} 1&0&\Delta t&0\ 0&1&0&\Delta t\ 0&0&1&0\ 0&0&0&1 \end{bmatrix} $$

and (\mathbf{w}_k) represents process uncertainty.

The filter accounts for the actual elapsed time between observations rather than assuming that every frame arrives at a fixed interval.

This produces smoother position and velocity estimates than directly differentiating noisy detection coordinates.

Important distinction: Image-space velocity is measured in pixels per second. It cannot be interpreted directly as real-world speed without camera calibration and appropriate geometric assumptions.

3. Adaptive Measurement Confidence

A bounding box can change dramatically when an animal bends, turns, or moves its limbs.

To reduce the influence of unreliable detections, Wildlife Sentinel can adapt its measurement-noise covariance according to observable indicators such as:

Changes in bounding-box dimensions.
Segmentation-mask stability.
Detection confidence.
Tracking consistency.
Occlusion and visibility estimates.

The measurement model is:

\mathbf{z}_k

\mathbf{H}\mathbf{x}_k + \mathbf{v}_k $$

where (\mathbf{z}_k) contains the observed image position, (\mathbf{H}) maps the estimated state to the observed quantities, and (\mathbf{v}_k) represents measurement noise.

The measurement-noise covariance (\mathbf{R}_k) can be increased when the detection is considered less reliable.

This reduces the influence of uncertain measurements while allowing the filter to rely more heavily on its motion prediction.

A grounded torso anchor or a stable point derived from an animal's segmentation mask may further reduce sensitivity to posture changes. However, such an anchor must be validated for the animal categories and camera viewpoints being used.

4. Motion Anomaly Detection

Wildlife Sentinel continuously evaluates the estimated movement of tracked animals.

Potentially interesting events include:

Sudden acceleration.
Abrupt changes in movement direction.
Rapid movement following a stationary period.
Unusual group movement.
Unexpected disappearance or prolonged loss of tracking.
Coordinated flight-like movement among multiple animals.

The system can use statistical thresholds, temporal rules, or learned models to identify motion patterns that warrant further investigation.

For example, a sudden acceleration accompanied by rapid movement away from a nearby predator may warrant a higher investigation priority than acceleration alone.

The system can also evaluate the uncertainty of its predictions before generating an alert.

An anomaly is not proof of poaching, a predator attack, or a stampede. Wildlife Sentinel treats these events as potential indicators that require contextual assessment.

5. Uncertainty-Aware Tracking

Kalman filtering provides both an estimated state and an associated covariance matrix.

The covariance matrix (\mathbf{P}_k) describes uncertainty in the estimated state.

Wildlife Sentinel can use this information to identify when a track is becoming unreliable, such as when an animal is obscured by vegetation or has not been detected for several frames.

For each new observation, the system calculates the innovation:

$$ \mathbf{y}k = \mathbf{z}_k - \mathbf{H}\hat{\mathbf{x}}{k|k-1} $$

The innovation covariance is:

$$ \mathbf{S}k = \mathbf{H}\mathbf{P}{k|k-1}\mathbf{H}^{T} + \mathbf{R}_k $$

The squared Mahalanobis distance is:

$$ d_M^2 = \mathbf{y}_k^{T} \mathbf{S}_k^{-1} \mathbf{y}_k $$

This statistic measures how inconsistent an observation is with the predicted measurement, taking uncertainty into account.

Observations that exceed a selected gating threshold can be rejected or treated as uncertain during data association.

For a two-dimensional measurement, a threshold of approximately 9.21 corresponds to the 99th percentile of a chi-squared distribution with two degrees of freedom.

This is a statistical association criterion, not a universal threshold for determining whether an animal is behaving abnormally.

6. Physics-Informed Active Perception

Not every scene requires the same processing intensity.

Wildlife Sentinel is designed to support adaptive frame processing based on scene activity, tracking uncertainty, and the importance of the current event.

For example:

Low activity: Reduce expensive inference frequency when the scene is stable and existing tracks remain reliable.
Moderate activity: Increase processing frequency when animals begin moving or detection uncertainty rises.
High activity: Prioritize rapid processing when a potential threat or unusual movement pattern is detected.
Tracking recovery: Increase observation frequency when an animal disappears or a track becomes uncertain.

A lightweight motion detector can help identify changes between expensive inference passes.

However, reducing inference frequency creates a trade-off between computational efficiency and the probability of detecting brief events. The system must be evaluated to determine suitable sampling rates for the deployment environment.

An operating mode of one inference per second can be considered for suitable low-activity scenes, but its effectiveness must be validated against the target animals, camera frame rate, and event-detection requirements.

7. AI-Assisted Threat Assessment

Wildlife Sentinel can integrate an AI agent with access to explicitly defined tools for investigating candidate events.

An AWS-based implementation may use Amazon Bedrock with a supported language model and a tool-execution layer.

When the event engine identifies a candidate anomaly, the system can provide the agent with structured information such as:

Animal category and track identifier.
Recent motion history.
Estimated velocity and acceleration.
Tracking uncertainty.
Event timestamps.
Relevant image crops.
Camera identifier and available location metadata.

The agent can then request permitted operations through a controlled tool interface.

Potential tools include:

Retrieve a recent image crop.
Enhance image contrast for inspection.
Retrieve the animal's recent movement history.
Summarize an event timeline.
Request a higher-resolution frame.
Prepare an incident report.
Request a camera movement through an authorized PTZ controller.

The Model Context Protocol (MCP) can provide a standardized way to expose tools to compatible clients. Alternatively, the application can use its own structured tool-calling interface.

These are alternative integration choices; using AWS or an LLM does not automatically create an MCP implementation.

Safety and reliability: The agent should not independently declare a person guilty of poaching or trigger dangerous actions. Camera movements, external notifications, and operational responses should be controlled by explicit permissions and appropriate validation rules.

8. Event Reporting and Conservation Alerts

When an event satisfies the configured alert criteria, Wildlife Sentinel can create a structured incident record.

An incident may include:

Event identifier.
Timestamp.
Camera identifier.
Detected animal categories.
Track identifiers.
Estimated movement characteristics.
Confidence and uncertainty information.
Relevant image or video evidence.
Event classification and supporting observations.

For an AWS deployment, Amazon Simple Notification Service (Amazon SNS) can distribute notifications to configured endpoints.

Amazon S3 may be used to store incident images and video clips, while appropriate database services can store searchable event metadata.

Camera coordinates can be attached to the report when reliable location metadata is available.

A camera's known coordinates identify the camera location; they do not automatically establish an animal's exact geographical position.

The objective is to help conservation personnel prioritize footage for review rather than replace human judgment.

9. Wildlife Monitoring Dashboard

Wildlife Sentinel can provide a dashboard that displays:

Live or recent camera feeds.
Detected animals and their tracking identifiers.
Bounding boxes or segmentation masks.
Estimated image-space trajectories.
Motion and acceleration plots.
Tracking confidence and uncertainty.
Detected anomalies.
Event history and supporting evidence.
Camera status and processing performance.

The dashboard allows users to inspect an alert and understand why the system generated it.

For a cloud-connected deployment, the architecture can stream structured telemetry to AWS IoT Core or another appropriate messaging layer.

Amazon Timestream can store supported time-series measurements, and Amazon Managed Grafana can visualize those measurements.

The exact AWS services should be selected according to the data model, retention requirements, supported integrations, and deployment cost.

How We Built It

Wildlife Sentinel is organized into modular components so that detection, tracking, event analysis, cloud integration, and visualization can be developed and tested independently.

Core Technology Stack

Computer Vision

Python and OpenCV.
YOLO-based animal detection.
Optional instance segmentation.
Multi-object tracking.
Image preprocessing and evidence extraction.

Motion Estimation

Linear state-space models.
Kalman filtering.
Variable-time-step state prediction.
Measurement uncertainty estimation.
Mahalanobis-distance gating.
Temporal motion analysis.

Backend and Event Processing

Python-based processing services.
Structured event records.
Configurable anomaly detection.
REST APIs or message-based communication where required.

AI Agent Integration

Amazon Bedrock with a supported model.
A controlled tool-execution interface.
Optional MCP integration.
Event summarization and evidence retrieval.

Cloud Infrastructure

Amazon SNS for notifications.
Amazon S3 for evidence storage, when configured.
AWS IoT Core for device messaging, when required.
Amazon Timestream for time-series telemetry, when appropriate.
Amazon Managed Grafana for visualization, when configured.

Camera Integration

RTSP-compatible cameras.
Recorded video for testing.
Optional PTZ camera control through a supported interface.

The architecture is modular: a local-only installation can perform detection, tracking, motion analysis, and dashboard visualization without requiring every cloud component.

Cloud services and AI-agent capabilities can be enabled when connectivity, budget, and operational requirements justify them.

System Architecture

             ┌─────────────────────────┐
             │ Wildlife Camera / RTSP  │
             │   or Recorded Video     │
             └────────────┬────────────┘
                          │
                          ▼
             ┌─────────────────────────┐
             │ Frame Acquisition       │
             │ Timestamp Management    │
             └────────────┬────────────┘
                          │
                          ▼
             ┌─────────────────────────┐
             │ Activity-Based Scheduler│
             │ Adaptive Inference Rate │
             └────────────┬────────────┘
                          │
                          ▼
             ┌─────────────────────────┐
             │ YOLO Animal Detection   │
             │ Optional Segmentation   │
             └────────────┬────────────┘
                          │
                          ▼
             ┌─────────────────────────┐
             │ Multi-Object Tracking   │
             │ Track Association       │
             └────────────┬────────────┘
                          │
                          ▼
       ┌──────────────────────────────────┐
       │ Motion Estimation Engine         │
       │                                  │
       │ • Kalman State Prediction        │
       │ • Measurement Update             │
       │ • Uncertainty Estimation          │
       │ • Innovation Gating              │
       └────────────────┬─────────────────┘
                        │
                        ▼
       ┌──────────────────────────────────┐
       │ Motion and Event Analysis        │
       │                                  │
       │ • Acceleration Changes           │
       │ • Direction Changes              │
       │ • Group Movement                 │
       │ • Track Reliability              │
       └────────────────┬─────────────────┘
                        │
              ┌─────────┴─────────┐
              │                  │
              ▼                  ▼
   ┌──────────────────┐  ┌──────────────────┐
   │ Local Dashboard  │  │ Event Orchestrator│
   │ Tracks & History │  │ Evidence Retrieval│
   └──────────────────┘  └─────────┬────────┘
                                   │
                                   ▼
                        ┌────────────────────┐
                        │ Optional AI Agent  │
                        │ Bedrock / MCP      │
                        └─────────┬──────────┘
                                  │
                                  ▼
                        ┌────────────────────┐
                        │ Validated Actions  │
                        │ Reports / PTZ / SNS│
                        └─────────┬──────────┘
                                  │
                                  ▼
                        ┌────────────────────┐
                        │ Optional AWS Data  │
                        │ Storage & Analytics│
                        └────────────────────┘

This architecture separates deterministic motion estimation from higher-level event interpretation. The motion engine generates measurable observations, while the event orchestrator determines whether further investigation is justified.

The Kinematics Engine: Mathematical Foundation

State Transition Model

For a two-dimensional constant-velocity model, the state vector is:

$$ \mathbf{x}k = \begin{bmatrix} x_k\ y_k\ v{x,k}\ v_{y,k} \end{bmatrix} $$

For an elapsed time (\Delta t):

$$ \mathbf{F}(\Delta t)= \begin{bmatrix} 1&0&\Delta t&0\ 0&1&0&\Delta t\ 0&0&1&0\ 0&0&0&1 \end{bmatrix} $$

The predicted state is:

\hat{\mathbf{x}}_{k|k-1}

\mathbf{F}(\Delta t) \hat{\mathbf{x}}_{k-1|k-1} $$

The predicted covariance is:

\mathbf{P}_{k|k-1}

\mathbf{F}\mathbf{P}_{k-1|k-1}\mathbf{F}^{T} + \mathbf{Q}(\Delta t) $$

Here, (\mathbf{Q}) represents process uncertainty.

Process Noise for Continuous White-Noise Acceleration

If acceleration is modeled as continuous white noise with spectral density (q), the exact discretized process-noise covariance for the state ordering ([x,y,v_x,v_y]^T) is:

$$ \mathbf{Q}(\Delta t)=q \begin{bmatrix} \frac{\Delta t^3}{3}&0&\frac{\Delta t^2}{2}&0\ 0&\frac{\Delta t^3}{3}&0&\frac{\Delta t^2}{2}\ \frac{\Delta t^2}{2}&0&\Delta t&0\ 0&\frac{\Delta t^2}{2}&0&\Delta t \end{bmatrix} $$

This formulation assumes continuous white acceleration noise and uses a noise spectral density with units consistent with the state variables.

A different model assumes a single random acceleration that remains constant during each sampling interval. Under that assumption, if the acceleration variance is (\sigma_a^2), the covariance is:

$$ \mathbf{Q}(\Delta t)= \sigma_a^2 \begin{bmatrix} \frac{\Delta t^4}{4}&0&\frac{\Delta t^3}{2}&0\ 0&\frac{\Delta t^4}{4}&0&\frac{\Delta t^3}{2}\ \frac{\Delta t^3}{2}&0&\Delta t^2&0\ 0&\frac{\Delta t^3}{2}&0&\Delta t^2 \end{bmatrix} $$

These matrices correspond to different assumptions about the acceleration process. They should not be treated as interchangeable without adjusting the model and noise parameter definitions.

Measurement Update

For measurements of the two-dimensional image position:

$$ \mathbf{z}k = \begin{bmatrix} x{\text{obs}}\ y_{\text{obs}} \end{bmatrix} $$

the measurement matrix is:

$$ \mathbf{H}= \begin{bmatrix} 1&0&0&0\ 0&1&0&0 \end{bmatrix} $$

The Kalman gain is:

$$ \mathbf{K}k = \mathbf{P}{k|k-1}\mathbf{H}^{T} \left( \mathbf{H}\mathbf{P}_{k|k-1}\mathbf{H}^{T} +\mathbf{R}_k \right)^{-1} $$

The corrected state estimate is:

\hat{\mathbf{x}}_{k|k}

\hat{\mathbf{x}}{k|k-1} + \mathbf{K}_k \left( \mathbf{z}_k- \mathbf{H}\hat{\mathbf{x}}{k|k-1} \right) $$

The covariance can be updated using the numerically robust Joseph form:

$$ \begin{aligned} \mathbf{P}{k|k} ={}& (\mathbf{I}-\mathbf{K}_k\mathbf{H}) \mathbf{P}{k|k-1} (\mathbf{I}-\mathbf{K}_k\mathbf{H})^T\ &+\mathbf{K}_k\mathbf{R}_k\mathbf{K}_k^T \end{aligned} $$

This approach supports variable observation intervals and provides an explicit representation of state uncertainty.

Handling Low-Speed Heading Instability

Movement direction is calculated from the estimated velocity:

$$ \theta_k=\operatorname{atan2}(v_y,v_x) $$

When the speed approaches zero, the direction becomes unreliable.

Wildlife Sentinel can use a low-speed threshold to suppress heading updates while an animal is nearly stationary.

When smoothing direction, it is preferable to average the unit-vector components rather than directly averaging angles:

$$ c_k=\cos\theta_k,\qquad s_k=\sin\theta_k $$

The smoothed components can be normalized before calculating the direction again:

$$ \bar{\theta}_k= \operatorname{atan2}(\bar{s}_k,\bar{c}_k) $$

This avoids the discontinuity associated with angles near (-\pi) and (+\pi).

A separate threshold for reactivating heading updates can prevent rapid switching when the estimated speed fluctuates around the minimum reliable speed.

Important Limitations

Kalman filtering improves state estimation but does not automatically distinguish animal body motion from camera motion, solve identity switches, or eliminate all detection errors.

Its performance depends on:

Measurement quality.
Motion-model suitability.
Correct time intervals.
Appropriate process-noise and measurement-noise parameters.
Reliable association between detections and tracks.

Ground-plane calibration and camera-motion compensation are necessary when the system must estimate physical animal speed from moving cameras or convert image motion into real-world units.

Challenges We Addressed

1. Bounding-Box Jitter

An animal's bounding box may shift even when the animal's overall body position changes very little.

Our approach uses temporal state estimation to reduce the influence of isolated position fluctuations.

Adaptive measurement noise can further reduce the impact of unreliable detections when the system has sufficient evidence that the measurements have become less stable.

2. Variable Frame Intervals

Video processing systems do not always process frames at perfectly regular intervals.

Using the actual elapsed time in the transition model allows the motion estimator to predict state changes over different intervals.

This is more reliable than assuming a fixed frame rate when calculating motion.

3. Unstable Direction Estimates

Direction estimates become particularly sensitive to noise when animals move slowly.

A low-speed threshold, vector-based angular smoothing, and uncertainty-aware updates help prevent misleading changes in heading.

4. Occlusion and Track Recovery

Vegetation and other obstacles can temporarily hide animals.

Innovation gating helps reject measurements that are inconsistent with a predicted track. Track-management logic can preserve tracks through brief occlusions and reinitialize them when uncertainty becomes excessive.

However, a large innovation does not necessarily indicate an incorrect detection. A real sudden movement can also produce a large residual. The system therefore needs appropriate gating, track-recovery, and re-identification policies.

5. Computational Efficiency

Expensive inference can be reduced during suitable low-activity periods.

An activity-based scheduler can adjust inference frequency according to scene changes and tracking uncertainty.

The design must also account for short-lived events that might occur between inference passes. Computational savings and event-detection performance should be measured together rather than optimized independently.

Accomplishments We Aim to Demonstrate

Wildlife Sentinel combines several technical components into one conservation-oriented system.

Physics-Informed State Estimation

The system uses a state-space model to estimate animal position and velocity over time instead of relying solely on frame-to-frame coordinate differences.

Uncertainty-Aware Motion Analysis

The filter covariance and innovation statistics provide useful information about tracking reliability and measurement consistency.

Adaptive Vision Processing

Activity-based inference scheduling offers a practical way to trade processing cost against temporal detection coverage.

AI-Assisted Event Investigation

A tool-enabled AI agent can retrieve relevant evidence and summarize candidate incidents, provided that the integration is implemented and validated.

Modular Cloud Architecture

The system can be extended with cloud messaging, event storage, notifications, and time-series visualization without coupling every component to the tracking engine.

Quantitative Evaluation

The following measurements are proposed for validating the implementation:

Metric	Evaluation Method
Position-estimation error	Compare estimated positions with manually annotated ground truth
Velocity-estimation error	Compare estimated velocities with reference trajectories
False motion-anomaly rate	Count false alerts per hour of annotated footage
Event recall	Measure the proportion of labeled events detected
Detection latency	Measure time from event onset to alert generation
Tracking performance	Evaluate identity switches, track fragmentation, and established tracking metrics
Inference reduction	Compare the number of model inferences against a fixed-rate baseline
Compute utilization	Record CPU, accelerator, memory, and power usage where available
Cloud cost	Compare measured service consumption under equivalent workloads

No numerical performance result should be reported until the corresponding experiment has been performed.

What We Learned

1. Detection Is Not the Same as Understanding Motion

An object detector identifies visual patterns, but dependable motion estimates require temporal reasoning.

Combining detection with a suitable state estimator creates a more useful representation of an animal's movement.

2. Uncertainty Is Valuable Information

A position estimate without uncertainty can create false confidence.

Tracking covariance helps the system recognize when its predictions are becoming less reliable and when more observations may be necessary.

3. Efficient AI Requires Intelligent Scheduling

Running an expensive model continuously is not always the most efficient strategy.

Adaptive inference can reduce unnecessary processing, but only when the scheduler accounts for missed-event risk and the required response time.

4. Classical Estimation and Generative AI Have Different Roles

A Kalman filter provides a mathematically defined motion estimate. An AI agent can help retrieve evidence, summarize an event, and coordinate permitted tools.

Separating these responsibilities makes the system easier to test, understand, and maintain.

5. Conservation Alerts Must Be Evidence-Based

An unusual movement pattern is not sufficient evidence of poaching or an animal attack.

Reliable incident assessment requires contextual information, appropriate confidence measures, and human review.

What's Next for Wildlife Sentinel?

1. Ground-Plane Calibration

We plan to investigate camera calibration and ground-plane homography to convert image coordinates into approximate physical coordinates.

This will support more meaningful estimates of animal speed and distance in calibrated, fixed-camera environments.

A single homography may not accurately model animals at different heights or scenes with significant terrain variation, so its limitations will need to be addressed.

2. Camera-Motion Compensation

For moving cameras, including drones and PTZ systems, background optical flow and robust geometric estimation can help distinguish camera motion from independent animal movement.

We plan to investigate Lucas–Kanade optical flow with RANSAC-based motion estimation.

3. Multi-Camera Tracking

We aim to explore cross-camera animal re-identification, overlapping fields of view, and track handoffs between cameras.

This could help construct a more complete picture of wildlife movement across larger protected areas.

4. Acoustic Sensor Fusion

Integrating acoustic sensors could provide additional context for unusual events.

Potential signals include gunshot-like sounds, vehicle engines, and other relevant environmental audio.

Audio classifications would be treated as supporting evidence rather than definitive proof of a threat.

5. Multi-Animal Behavioral Analysis

Future work could incorporate group-level motion models to identify coordinated changes in direction, collective flight, and other patterns that may warrant investigation.

Such models would require species-specific evaluation to avoid treating normal collective behavior as a threat.

6. Edge Deployment

We plan to investigate deployment on energy-efficient edge computers located close to wildlife cameras.

Local processing could reduce dependence on continuous cloud connectivity, lower video-transfer requirements, and support operation in remote environments.

Cloud services could remain available for incident reporting, centralized monitoring, and longer-term analytics.

Why Wildlife Sentinel Matters

Wildlife conservation requires more than detecting animals in individual images. It requires reliable interpretation of changing situations across time.

Wildlife Sentinel brings together computer vision, mathematical state estimation, uncertainty-aware event detection, adaptive processing, and optional AI-assisted investigation.

Its central idea is straightforward:

Use physics-informed motion estimation to decide what deserves attention, then use intelligent evidence retrieval and event reporting to help conservation teams investigate it.

By treating uncertainty as part of the problem and separating measurable motion from higher-level interpretation, Wildlife Sentinel provides a foundation for building more efficient and explainable wildlife-monitoring systems.

Our long-term goal is to help conservation teams spend less time reviewing uneventful footage and more time investigating situations that genuinely require attention.

Built With

agentic-vision
amazon-bedrock
amazon-timestream
amazon-web-services
aws-graviton
aws-iot-core
computer-vision
edge-computing
grafana
iot
kalman-filter
machine-learning
opencv
python
wildlife-conservation