GaitAI · SecureVision

GaitAI SuspiciousMotion

Name: GaitAI SuspiciousMotion
Brand: GaitAI

Anomalies surfaced — without identity matching as a prerequisite.

SuspiciousMotion surfaces candidate movement events — loitering, running, restricted-zone entry and tailgating-like patterns — from camera streams for operator review, using movement-first processing that does not require identity recognition.

Airports, metro & rail Corporate & university campuses Factories & warehouses Malls & retail Defence & Armed Forces

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Input

Camera stream

Analysis

Behaviour analysis

Output

Event alerts

User

Security operator

Problem

Security teams cannot manually watch every camera continuously, and many safety events are defined by movement rather than identity.

What GaitAI does

Person and pose tracking converts streams into movement trajectories; temporal behaviour analysis flags patterns that deviate from configured norms and surfaces them as timeline events with alerts.

Who uses it

Campuses and universities
Offices and enterprise sites
Malls and retail complexes
Hospitals
Transport hubs
Factory security teams

What they receive

Loitering alertRunning alertRestricted-zone eventTailgating indicatorPerimeter eventEvent timeline

Movement-first

Detection basis

Event types (example config)

Per-zone

Rule configuration

Timeline

Evidence format

Example report values — illustrative, not measured performance claims.

Alerts are indicators for a trained operator to review — an anomaly is a deviation from configured movement norms, not an accusation. Escalation remains a human decision.

Why it matters

Movement-defined events surface for operator review instead of being found hours later in recorded footage — and initial processing works with identity matching optional and governed.

Example workflow

01Cameras stream into the movement pipeline
02People are tracked as appearance-reduced trajectories
03Behaviour analysis compares movement against configured rules and norms
04Anomalous events post to the operator timeline
05Operator reviews and responds

Deployment

Works with existing fixed cameras
Zone and rule configuration per site
Operator timeline and alert routing
PrivacyGuard controls apply platform-wide

Research basis

Published work is the research foundation for the capabilities this product is built on. That record covers the movement-analysis methods themselves — it is not a validation of this product's outputs, and there is no clinical validation study, accuracy benchmark or trial result behind them.

Privacy-aware analytics: Privacy-preserving gait data
Edge inference: Edge gait analytics (Patent 402202)

Research foundation · 2 records

Full evidence map

Evidence status

GaitAI Evidence Index

4 categories documented

What is documented for this module, and what is not. Research foundation is not product validation — the two are listed separately on purpose.

Research foundationAvailable: 1 published record directly addresses a capability this module is built on (Privacy-preserving gait data).
View evidence for Research foundation
Claim / capability: 1 published record directly addresses a capability this module is built on (Privacy-preserving gait data).
Status: Available
Applicability: The cited work is specifically about a method or movement signal this module draws on.
Limitation: Research relevance is not evidence that this product's outputs have been validated.
publicationProtection of gait data set for preserving its privacy in deep learning pipeline · IET Biometrics, 2022
Inventory reviewed 2026-09-13.
Architectural research relationshipAvailable: 1 published record informs shared platform capabilities this module uses (Edge gait analytics (Patent 402202)). The research does not address this module's application specifically.
View evidence for Architectural research relationship
Claim / capability: 1 published record informs shared platform capabilities this module uses (Edge gait analytics (Patent 402202)). The research does not address this module's application specifically.
Status: Available
Applicability: Shared capabilities such as pose estimation or temporal modelling are used by many modules.
Limitation: A shared-platform relationship is the weakest research link and must not be read as validating this module.
publicationA Covariate-Based Gait Recognition System and Method for Edge Analytics Using Optimized Deep Learning Pipeline · Patent — Government of India, 2022
Inventory reviewed 2026-09-13.
Product specificationAvailable: The module has a documented specification: inputs, pipeline, outputs and stated limitations.
View evidence for Product specification
Claim / capability: The module has a documented specification: inputs, pipeline, outputs and stated limitations.
Status: Available
Applicability: Documented inputs, intended workflow and output design.
Limitation: Documentation does not establish a released implementation or deployment readiness.
specificationProduct documentation
Inventory reviewed 2026-09-13.
Product implementation evidenceNot yet published: No module-specific release or deployment record is published here.
View evidence for Product implementation evidence
Claim / capability: No module-specific release or deployment record is published here.
Status: Not yet published
Applicability: Production implementation is distinct from this website's interactive prototypes.
Limitation: The browser pose explorer is not the full clinical or operational product pipeline.
Context · not validation evidenceProduct and deployment context
Inventory reviewed 2026-09-13.
Interactive demoAvailable: A sample output is available to explore. Illustrative demo — example values, not a measured result.
View evidence for Interactive demo
Claim / capability: A sample output is available to explore. Illustrative demo — example values, not a measured result.
Status: Available
Applicability: An interactive explanation of the intended output and workflow.
Limitation: Sample outputs contain illustrative values and are not measurements of a visitor or product performance.
prototypeExplore illustrative output
Inventory reviewed 2026-09-13.
Benchmark validationNot yet published: Validation pending publication. No product benchmark numbers are supplied.
View evidence for Benchmark validation
Claim / capability: Validation pending publication. No product benchmark numbers are supplied.
Status: Not yet published
Applicability: A benchmark needs a defined dataset, sample count, environment, hardware and evaluation method.
Limitation: Accuracy, sensitivity, specificity, latency and FPS cannot be inferred from a demonstration.
Context · not validation evidenceResearch evidence inventory
Inventory reviewed 2026-09-13.
Product-specific validationNot yet published: No study in the published record evaluates this module's output for a particular intended use.
View evidence for Product-specific validation
Claim / capability: No study in the published record evaluates this module's output for a particular intended use.
Status: Not yet published
Applicability: Validation must match the module, population and intended decision.
Limitation: A product description or related research paper does not establish validated performance.
Context · not validation evidencePublished evidence inventory
Inventory reviewed 2026-09-13.
Pilot & deployment evidenceNot yet published: No named pilot, deployment result or customer outcome is documented.
View evidence for Pilot & deployment evidence
Claim / capability: No named pilot, deployment result or customer outcome is documented.
Status: Not yet published
Applicability: Pilot and deployment evidence is assessed separately from research.
Limitation: Use-case journeys describe intended workflows, not customer results.
Context · not validation evidenceTrust Center
Inventory reviewed 2026-09-13.
Product-specific clinical validationNot yet published: No product-specific clinical validation study is published in this library.
View evidence for Product-specific clinical validation
Claim / capability: No product-specific clinical validation study is published in this library.
Status: Not yet published
Applicability: Clinical applicability depends on the intended use; safety modules are not presented as clinical tools.
Limitation: No output replaces clinician judgement or establishes a diagnosis.
Context · not validation evidenceResponsible use and intended context
Inventory reviewed 2026-09-13.
Regulatory clearanceNot claimed: No medical-device clearance, regulatory approval or compliance status is claimed.
View evidence for Regulatory clearance
Claim / capability: No medical-device clearance, regulatory approval or compliance status is claimed.
Status: Not claimed
Applicability: Regulatory applicability depends on the intended use and deployment jurisdiction.
Limitation: Research publications, patents and prototypes are not regulatory clearances.
Context · not validation evidenceWhat GaitAI does not claim
Inventory reviewed 2026-09-13.

Inventory reviewed 2026-09-13How GaitAI states evidence →

Sample output

Privacy & responsible use

Designed to support consent-based capture, encrypted transfer, configurable retention and role-based access, with activity logging and skeleton-only processing modes. Deployment requirements depend on the applicable environment, organization and jurisdiction. Privacy-aware architecture does not automatically mean anonymity: pose, gait and trajectories may still carry identifying information. Movement analytics are designed to run without identifying individuals unless a deployment lawfully and explicitly requires it, and identity-related capabilities are intended only for lawful, authorized deployments with appropriate governance, access control and auditability. Outputs are decision support for trained operators — not autonomous enforcement.

Context & limitations

Detects movement patterns, not intent — operator review is required
Coverage and lighting constrain tracking quality
Initial detection is privacy-aware by design

GaitAI · SuspiciousMotion

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