> ## Documentation Index
> Fetch the complete documentation index at: https://docs.videodb.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Multi-Camera Public Safety Surveillance
> AI-powered 4-camera surveillance system for public safety monitoring with multi-angle alerting
## The Challenge
Modern cities face growing challenges in ensuring public safety. From crowded metros to busy intersections, monitoring many locations in real-time is tough.
Traditional systems rely on humans watching multiple screens — costly, error-prone, and unable to correlate events across cameras.
What if AI could monitor all feeds, detect incidents instantly, and alert authorities automatically?
## What You'll Build
VideoDB RTStream brings AI-powered intelligence to multi-camera systems. In this guide, you'll build a surveillance network that:
* Connects 4 camera feeds across a public space
* Detects 3 types of security incidents simultaneously
* Monitors 12 event streams (4 cameras × 3 events)
* Generates 2×2 multi-angle evidence for forensic analysis
* Sends real-time alerts to security authorities
## 4-Camera Surveillance Network
```
🏙️ City Public Square - 4 Camera System
├── CAM 1: Plaza Overview (High Angle - East)
│ └── rtsp://samples.rts.videodb.io:8554/pub-cam1
│
├── CAM 2: Main Walkway (Close Up)
│ └── rtsp://samples.rts.videodb.io:8554/pub-cam2
│
├── CAM 3: Stairway Junction
│ └── rtsp://samples.rts.videodb.io:8554/pub-cam3
│
└── CAM 4: Central Plaza (Mid Angle)
└── rtsp://samples.rts.videodb.io:8554/pub-cam4
```
## Setup
### Install Dependencies
```bash theme={null}
pip install videodb
```
### Connect to VideoDB
```python theme={null}
import videodb
api_key = "your_api_key"
conn = videodb.connect(api_key=api_key)
coll = conn.get_collection()
```
## Implementation
### Step 1: Define All 4 Camera Streams
```python theme={null}
cameras = {
"plaza_overview": "rtsp://samples.rts.videodb.io:8554/pub-cam1",
"main_walkway": "rtsp://samples.rts.videodb.io:8554/pub-cam2",
"stairway_junction": "rtsp://samples.rts.videodb.io:8554/pub-cam3",
"central_plaza": "rtsp://samples.rts.videodb.io:8554/pub-cam4",
}
# Connect all camera streams
rtstreams = {}
for cam_name, rtsp_url in cameras.items():
rtstreams[cam_name] = coll.connect_rtstream(
name=f"Surveillance: {cam_name.replace('_', ' ').title()}",
url=rtsp_url,
store=True,
)
```
### Step 2: Create Shared Indexes on All Cameras
Define one comprehensive analysis prompt and create indexes on all 4 streams:
```python theme={null}
analysis_prompt = """Analyze this surveillance footage. Describe:
1. People with trolley bags, backpacks, or luggage
2. Crowd behavior (groups gathering, movement patterns)
3. Unusual objects (unattended items)
Be specific about location and appearance."""
scene_understandings = {}
for cam_name, rtstream in rtstreams.items():
understanding = rtstream.understand(
segmentation={"type": "time", "window": "10s"},
analyzers=[{
"type": "vlm",
"name": "scene",
"sampling": {"frame_count": 1},
"config": {"prompt": analysis_prompt},
}],
store=True,
)
scene_understandings[cam_name] = {
"understanding": understanding,
"output": understanding.outputs.get("scene"),
}
scene_indexes = {}
for cam_name, rtstream in rtstreams.items():
scene = scene_understandings[cam_name]
index = rtstream.index(
source=scene["output"],
name=f"Surveillance_{cam_name.upper()}",
use_for=["semantic"],
)
scene_indexes[cam_name] = {
"understanding": scene["understanding"],
"output": scene["output"],
"index": index,
"index_id": index.id,
}
```
### Step 3: Define 3 Shared Security Events
Create events that apply across all cameras:
```python theme={null}
# Unattended Luggage (High Priority)
luggage_event_id = conn.create_event(
event_prompt="Detect luggage, backpacks, or packages left unattended.",
label="unattended_luggage",
)
# Large Crowd Formation (Alert)
crowd_event_id = conn.create_event(
event_prompt="Identify when >4 people gather quickly in a concentrated area.",
label="large_crowd_formation",
)
# Person with Trolley (Tracking)
trolley_event_id = conn.create_event(
event_prompt="Detect any person with trolley bag or rolling suitcase.",
label="person_with_trolley",
)
```
### Step 4: Attach Alerts to All 4 Cameras
```python theme={null}
webhook_url = "https://your-security-center-webhook.com"
event_ids = {
"luggage": luggage_event_id,
"crowd": crowd_event_id,
"trolley": trolley_event_id,
}
# Create alerts for all 3 events on all 4 cameras (12 total alerts)
for cam_name, scene_index in scene_indexes.items():
for event_type, event_id in event_ids.items():
scene_index["index"].create_alert(event_id, callback_url=webhook_url)
```
Now you have a comprehensive 4-camera × 3-event surveillance grid (12 alert streams).
## Illustrative Alert Envelopes
These examples show a possible live payload. Inspect the received alert before relying on optional fields.
### Unattended Luggage Alert (Critical Priority)
```json theme={null}
{
"event_id": "event-luggage-001",
"label": "unattended_luggage",
"confidence": 0.96,
"explanation": "A black backpack is detected on the plaza ground with no person visibly attached to it. Has been stationary for >1 minute.",
"timestamp": "2025-09-10T17:02:15.811085+00:00",
"start_time": "2025-09-10T22:31:50.886736+05:30",
"end_time": "2025-09-10T22:32:05.886736+05:30",
"stream_url": "https://rt.stream.videodb.io/manifests/rts-019711db-1086-7750-ba79-8f47a4fed603/1757523710000000-1757523726000000.m3u8"
}
```
### Large Crowd Formation Alert
```json theme={null}
{
"event_id": "event-crowd-001",
"label": "large_crowd_formation",
"confidence": 0.92,
"explanation": "Multiple individuals (8+) rapidly gathering in central plaza area. Unusual congregation pattern.",
"timestamp": "2025-09-10T17:15:42.123456+00:00",
"start_time": "2025-09-10T22:45:30.000000+05:30",
"end_time": "2025-09-10T22:45:45.000000+05:30",
"stream_url": "https://rt.stream.videodb.io/manifests/rts-019711db-1086-7750-ba79-8f47a4fed603/1757524530000000-1757524545000000.m3u8"
}
```
### Person with Trolley Alert (Tracking)
```json theme={null}
{
"event_id": "event-trolley-001",
"label": "person_with_trolley",
"confidence": 0.95,
"explanation": "Female with black rolling suitcase detected moving through plaza.",
"timestamp": "2025-09-10T17:02:15.811085+00:00",
"start_time": "2025-09-10T22:31:50.886736+05:30",
"end_time": "2025-09-10T22:32:05.886736+05:30",
"stream_url": "https://rt.stream.videodb.io/manifests/rts-019711db-1086-7750-ba79-8f47a4fed603/1757523710000000-1757523726000000.m3u8"
}
```
## Synchronized Multi-Angle Evidence
When an incident is detected, you can retrieve clips from all 4 cameras for the same requested interval and arrange them in a 2×2 view:
```python theme={null}
# Extract incident timestamp from alert
incident_epoch = 1757523730 # from alert data
# Generate four clips for the same requested interval (±10 seconds)
time_window = 10
evidence_clips = {}
for cam_name, rtstream in rtstreams.items():
rtstream.generate_stream(
start=incident_epoch - time_window,
end=incident_epoch + time_window
)
evidence_clips[cam_name] = rtstream.stream_url
# Compose the four HLS clips in a 2×2 layout for review.
```
Use clock-aligned camera sources before treating clips from the same requested interval as synchronized evidence.
## The Result
This demo shows how AI turns raw surveillance feeds into actionable intelligence for:
* **Public safety monitoring** across wide areas
* **Crowd management** and anomaly detection
* **Security threat detection** in real-time
* **Forensic investigation** with multi-angle evidence
* **Real-time incident response** with 2×2 multi-angle playback
Key capabilities:
* **12 Event Streams** - Coverage of 4 camera angles with 3 security events each
* **Sub-Second Alerts** - Instant notification when incidents occur
* **Multi-Angle Evidence** - Synchronized clips from all cameras for investigations
* **Scalability** - Pattern can extend to 10, 20, or 100+ cameras
* **24/7 Monitoring** - Fully automated, no human fatigue
Open the complete implementation with webhook setup, ngrok integration, and forensic analysis.
## Related Tutorials
Intelligent security system with tiered threat detection
Real-time sports analysis with synchronized playback
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