Release Notes#

Current Release#

Version 1.1.0#

Release Date: May 2026

New:

  • Detecting suspicious behavior:

    Activity

    Trigger

    Alert Level

    Merchandise Concealment

    Pose + VLM confirms suspicious behavior

    WARNING

    Checkout Bypass

    Visited HIGH_VALUE zone, exited without CHECKOUT

    WARNING / CRITICAL

    Loitering

    Dwell time exceeds threshold in HIGH_VALUE zone

    WARNING

    Repeated Visits

    Re-entries ≥ threshold to the same HIGH_VALUE zone

    WARNING

    Restricted Zone Violation

    Entered a RESTRICTED zone

    CRITICAL

  • Declarative rule engine: Rules configuration moved from code to declarative configs/rules.yaml with variable substitution, session flags, and conditional alert severity escalation — no code changes needed to add or modify detection rules

  • Behavioral analysis with VLM: Two-stage pipeline using YOLO pose estimation followed by Qwen2.5-VL-7B-Instruct VLM inference via OVMS for concealment detection in high-value zones

  • Session flag system: Auto-set boolean flags (e.g., visited_high_value, concealment_suspected) based on zone visits or external service results, usable as rule conditions

  • Checkout bypass detection: Rule that fires when a person visits a high-value zone and exits without passing through checkout, with severity escalated to CRITICAL if concealment was detected

  • Dynamic SceneScape configuration: All environment variables are now auto-generated from configs/zone_config.json via make init — eliminates manual .env editing

  • Scene export script: make export-scene exports scene config from a running SceneScape instance as an importable zip file

  • Per-camera pipeline configs: DL Streamer pipeline configs are generated dynamically per camera from zone_config.json

  • Benchmark submodule: Benchmark targets now use performance-tools submodule instead of inline scripts.

  • Stream density benchmarking: Integrated performance-tools submodule with make benchmark, make benchmark-stream-density, and make consolidate-metrics targets

  • App-specific controller configs: Tracker and reid configs moved from SceneScape to each app’s configs/ directory (SAD: L2/30)

  • Device resource configs: Selectable device profiles (all-gpu-cpu.env, all-npu-cpu.env, etc.) via DEVICE= parameter with automatic validation

Fixed:

  • Fixed behavioral analysis timeout when VLM inference exceeds default HTTP timeout

  • Fixed rule engine variable substitution not applying environment overrides

  • Fixed Gradio UI health check failing on startup race condition

Version 1.0.0#

Release Date: April 2026

Features:

  • Rule-based suspicious activity detection using Intel® SceneScape zone events (entry, exit, loiter) with configurable thresholds

  • Restricted zone violation alerts — immediate CRITICAL alert on entry to RESTRICTED zones

  • Repeated high-value zone visit detection with configurable visit count threshold

  • Loitering detection — alerts when dwell time in high-value zones exceeds threshold

  • Behavioral analysis service with YOLO pose estimation and VLM-based concealment detection using OpenVINO™ Model Server

  • Multi-strategy alert delivery via dedicated alert service — MQTT publish, WebSocket, and logging with configurable handlers

  • Gradio UI for real-time alert monitoring with zone map visualization

  • Person session tracking with automatic timeout and zone visit history

  • Frame capture from SceneScape cameras with configurable cadence and storage in SeaweedFS (S3-compatible object storage)

  • Docker Compose deployment with services: swlp-service, behavioral-analysis, alert-service, Gradio UI, SeaweedFS, and OVMS-VLM

  • Integration with Intel® SceneScape for multi-camera person tracking and zone events

OpenVINO™ Models Used:

Model

Purpose

Output

yolov8s

Person detection (DL Streamer)

Person bounding boxes

person-reidentification-retail-0277

Person re-identification

Embedding vector

yolo26n-pose

Pose estimation

Skeleton keypoints

Qwen/Qwen2.5-VL-7B-Instruct

Visual language model (VLM)

Concealment classification

HW Used for Validation:

  • Intel® Xeon® Scalable Processor (4th Generation)

  • Intel® Arc™ GPU (for VLM inference via OVMS)

  • Ubuntu 22.04 LTS

Known Issues/Limitations:

  • VLM inference latency can be 5–30 seconds per request depending on GPU load; behavioral analysis results may lag behind real-time events.

  • SeaweedFS frame storage requires sufficient disk space; configure evidence_retention_hours in app_config.json to manage retention.

  • SceneScape integration is required for all zone-based detection rules; without SceneScape, no suspicious activity detection is possible.

  • The fire_once_per: session deduplication means a repeated-visit alert will not re-fire even if the person continues visiting the zone after the threshold is crossed.

  • OVMS VLM service requires GPU with sufficient VRAM for Qwen2.5-VL-7B-Instruct; NPU offload is supported via device config but may have reduced throughput.