SmartHome Video Anomaly Benchmark
Benchmark suite evaluating VLM models on video anomaly detection across 7 smart home categories — based on the SmartHome-Bench dataset (1,203 YouTube smart home camera clips).
Key differentiator from HomeSec-Bench: This benchmark requires video understanding (multi-frame sampling), not single-frame analysis.
Setup
Requires npm install + system dependencies. The deployment agent should:
- Check for
yt-dlpandffmpegin PATH - Run
npm installin the skill directory
Entry script: scripts/run-benchmark.cjs
Verification
bashnode scripts/run-benchmark.cjs --help
Quick Start
As an Aegis Skill (automatic)
When spawned by Aegis, configuration is injected via environment variables. The benchmark downloads video clips, samples frames, evaluates with VLM, and generates an HTML report.
Standalone
bash# Run with local VLM (subset mode, 50 videos) node scripts/run-benchmark.cjs --vlm http://localhost:5405 # Quick test with 10 videos node scripts/run-benchmark.cjs --vlm http://localhost:5405 --max-videos 10 # Full benchmark (all curated clips) node scripts/run-benchmark.cjs --vlm http://localhost:5405 --mode full # Filter by category node scripts/run-benchmark.cjs --vlm http://localhost:5405 --categories "Wildlife,Security" # Skip download (re-evaluate cached videos) node scripts/run-benchmark.cjs --vlm http://localhost:5405 --skip-download # Skip report auto-open node scripts/run-benchmark.cjs --vlm http://localhost:5405 --no-open
Configuration
Environment Variables (set by Aegis)
| Variable | Default | Description |
|---|---|---|
AEGIS_VLM_URL | (required) | VLM server base URL |
AEGIS_VLM_MODEL | — | Loaded VLM model ID |
AEGIS_SKILL_ID | — | Skill identifier (enables skill mode) |
AEGIS_SKILL_PARAMS | {} | JSON params from skill config |
Note: This is a VLM-only benchmark. An LLM gateway is not required.
User Configuration (config.yaml)
This skill includes a config.yaml that defines user-configurable parameters. Aegis parses this at install time and renders a config panel in the UI. Values are delivered via AEGIS_SKILL_PARAMS.
| Parameter | Type | Default | Description |
|---|---|---|---|
mode | select | subset | Which clips to evaluate: subset (~50 clips) or full (all ~105 curated clips) |
maxVideos | number | 50 | Maximum number of videos to evaluate |
categories | text | all | Comma-separated category filter (e.g. Wildlife,Security) |
noOpen | boolean | false | Skip auto-opening the HTML report in browser |
CLI Arguments (standalone fallback)
| Argument | Default | Description |
|---|---|---|
--vlm URL | (required) | VLM server base URL |
--out DIR | ~/.aegis-ai/smarthome-bench | Results directory |
--max-videos N | 50 | Max videos to evaluate |
--mode MODE | subset | subset or full |
--categories LIST | all | Comma-separated category filter |
--skip-download | — | Skip video download, use cached |
--no-open | — | Don't auto-open report in browser |
--report | (auto in skill mode) | Force report generation |
Protocol
Aegis → Skill (env vars)
AEGIS_VLM_URL=http://localhost:5405 AEGIS_SKILL_ID=smarthome-bench AEGIS_SKILL_PARAMS={}
Skill → Aegis (stdout, JSON lines)
jsonl{"event": "ready", "model": "SmolVLM2-2.2B", "system": "Apple M3"} {"event": "suite_start", "suite": "Wildlife"} {"event": "test_result", "suite": "Wildlife", "test": "smartbench_0003", "status": "pass", "timeMs": 4500} {"event": "suite_end", "suite": "Wildlife", "passed": 12, "failed": 3} {"event": "complete", "passed": 78, "total": 105, "timeMs": 480000, "reportPath": "/path/to/report.html"}
Human-readable output goes to stderr (visible in Aegis console tab).
Test Suites (7 Categories)
| Suite | Description | Anomaly Examples |
|---|---|---|
| 🦊 Wildlife | Wild animals near home cameras | Bear on porch, deer in garden, coyote at night |
| 👴 Senior Care | Elderly activity monitoring | Falls, wandering, unusual inactivity |
| 👶 Baby Monitoring | Infant/child safety | Stroller rolling, child climbing, unsupervised |
| 🐾 Pet Monitoring | Pet behavior detection | Pet illness, escaped pets, unusual behavior |
| 🔒 Home Security | Intrusion & suspicious activity | Break-ins, trespassing, porch pirates |
| 📦 Package Delivery | Package arrival & theft | Stolen packages, misdelivered, weather damage |
| 🏠 General Activity | General smart home events | Unusual hours activity, appliance issues |
Each clip is evaluated for binary anomaly detection: the VLM predicts normal (0) or abnormal (1), compared against expert annotations.
Metrics
Per-category and overall:
- Accuracy — correct predictions / total
- Precision — true positives / predicted positives
- Recall — true positives / actual positives
- F1-Score — harmonic mean of precision & recall
- Confusion Matrix — TP, FP, TN, FN breakdown
Results
Results are saved to ~/.aegis-ai/smarthome-bench/ as JSON. An HTML report with per-category breakdown, confusion matrix, and model comparison is auto-generated.
Requirements
- Node.js ≥ 18
npm install(foropenaiSDK dependency)yt-dlp(video download from YouTube)ffmpeg(frame extraction from video clips)- Running VLM server (must support multi-image input)
