GaitAI Watchlist
Lawful, audited, and intended only for authorized deployments.
Watchlist provides policy-governed candidate matching against an authorized watchlist, with confidence scoring, consent/policy logs and auditability. It is restricted to deployments with lawful authority — it is not offered for general-public surveillance.
Problem
Certain high-security environments may require controlled candidate matching against an authorized watchlist.
What GaitAI does
Movement-signature comparison against a lawfully constituted watchlist produces confidence-scored candidates for mandatory human review, inside a policy-governed, fully audited workflow.
Who uses it
- Authorized law-enforcement deployments
- Defence agencies
- Critical-infrastructure sites where lawful
What they receive
Example report values — illustrative, not measured performance claims.
A match is a confidence-scored candidate for adjudication — never an automatic identification or action trigger. Responsible deployment: this product is not offered for monitoring the general public, and outputs must not be treated as unrestricted identification.
Why it matters
Where the law authorises watchlist screening, the difference between responsible and irresponsible deployment is governance: candidate-only outputs, mandatory review, scoped lists, and an audit trail for every match and every access.
Example workflow
- 01Deployment authority and list governance are established first
- 02Watchlist entries are enrolled under policy
- 03Streams within the authorized scope are screened
- 04Candidates above threshold go to mandatory human review
- 05Every match, review and access is logged
Deployment
- Intended only for deployments with lawful authority and policy controls
- List governance: scoped entries, review cycles, removal process
- Mandatory human adjudication of candidates
- Audit trail available for oversight bodies
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.
- Movement biometrics
- Gait recognition & biometrics
- Person re-identification
- Gait recognition & biometrics
- Privacy-aware analytics
- Privacy-preserving gait data
- Advancements in Artificial Intelligence for Biometrics: A Deep Dive into Model-based Gait Recognition TechniquesEngineering Applications of Artificial Intelligence · 2024
- Journey into Gait Biometrics: Integrating Deep Learning for Enhanced Pattern RecognitionDigital Signal Processing · 2024
- Deep learning pipelines for recognition of gait biometrics with covariates: a comprehensive reviewArtificial Intelligence Review · 2023
- Data Preprocessing and Feature Selection Techniques in Gait Recognition: A Comparative Study of Machine Learning and Deep Learning ApproachesPattern Recognition Letters · 2023
- Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methodsNeurocomputing · 2022
- Protection of gait data set for preserving its privacy in deep learning pipelineIET Biometrics · 2022
Evidence status
GaitAI Evidence Index
2 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
- 6 published records directly address a capability this module is built on (Gait recognition & biometrics, Privacy-preserving gait data).
View evidence for Research foundation
Claim / capability: 6 published records directly address a capability this module is built on (Gait recognition & biometrics, 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.
- publicationAdvancements in Artificial Intelligence for Biometrics: A Deep Dive into Model-based Gait Recognition Techniques · Engineering Applications of Artificial Intelligence, 2024
- publicationJourney into Gait Biometrics: Integrating Deep Learning for Enhanced Pattern Recognition · Digital Signal Processing, 2024
- publicationDeep learning pipelines for recognition of gait biometrics with covariates: a comprehensive review · Artificial Intelligence Review, 2023
- publicationData Preprocessing and Feature Selection Techniques in Gait Recognition: A Comparative Study of Machine Learning and Deep Learning Approaches · Pattern Recognition Letters, 2023
- publicationIntra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methods · Neurocomputing, 2022
- publicationProtection of gait data set for preserving its privacy in deep learning pipeline · IET Biometrics, 2022
Inventory reviewed .
- Architectural research relationshipNot yet published
- No further shared-platform research relationship is recorded beyond the research foundation above.
View evidence for Architectural research relationship
Claim / capability: No further shared-platform research relationship is recorded beyond the research foundation above.
Status: Not yet published
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.
No supporting source is published for this status.
Inventory reviewed .
- 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 .
- 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 .
- Interactive demoNot yet published
- No sample output has been built for this module yet.
View evidence for Interactive demo
Claim / capability: No sample output has been built for this module yet.
Status: Not yet published
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.
No supporting source is published for this status.
Inventory reviewed .
- 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 .
- 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 .
- 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 .
- 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 .
- 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 .
Inventory reviewed How GaitAI states evidence →
Privacy & responsible use
Responsible deployment: Watchlist is restricted to deployments with lawful authority, governed lists, mandatory human adjudication and auditability. It is not offered for general-public surveillance, and its outputs must never be treated as unrestricted identification. 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. Watchlist, ReID, AccessMotion and ForensicSearch require documented lawful authority, consent where applicable, purpose-limited access, audit logs and human review. Candidate associations are not proof of identity and must not trigger automatic denial, enforcement or adverse decisions.
Context & limitations
- Candidates are not identity proof; adjudication is mandatory
- Restricted to lawful, authorized, scoped deployments
- Not offered for general-public surveillance
Related SecureVision products
All products
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