DeepCamera SmartHome Video Anomaly Benchmark

VLM evaluation suite for video anomaly detection in smart home camera footage

install

source · Clone the upstream repo

git clone https://github.com/SharpAI/DeepCamera

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/SharpAI/DeepCamera "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/analysis/smarthome-bench" ~/.claude/skills/sharpai-deepcamera-smarthome-video-anomaly-benchmark && rm -rf "$T"

manifest: skills/analysis/smarthome-bench/SKILL.md

source content

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-dlp
```
and
```
ffmpeg
```
in PATH
Run
```
npm install
```
in the skill directory

Entry script:

scripts/run-benchmark.cjs

Verification

node 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

# 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)

{"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
```
(for
```
openai
```
SDK dependency)
```
yt-dlp
```
(video download from YouTube)
```
ffmpeg
```
(frame extraction from video clips)
Running VLM server (must support multi-image input)

Citation

Based on SmartHome-Bench: A Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Foundation Models.