CCTV Abnormality & Crime Detection

CCTV Abnormality & Crime Detection

The CCTV Abnormality & Crime Detection model analyzes surveillance video footage to identify suspicious activities, abnormal behaviors, and potential criminal incidents. This model is designed for security applications where automated monitoring of video feeds is essential.

The Challenge

Security personnel cannot monitor all camera feeds continuously. With thousands of hours of footage generated daily, human operators miss critical events due to fatigue, distraction, or simply the overwhelming volume of data. By the time an incident is noticed, it's often too late for prevention.

Automated detection systems can monitor multiple feeds 24/7, alerting operators only when suspicious activity is detected. This allows security teams to focus their attention where it matters most, improving response times and reducing the risk of missed incidents.

Model Overview

This model uses advanced deep learning to provide fast and accurate crime detection. A two-stage pipeline enables rapid screening of normal footage while performing detailed analysis only when suspicious activity is detected.

The model achieves 91.6% F1 score with 94.3% recall in detecting abnormal activities across 17 crime categories.

How It Works

The model employs a two-stage detection pipeline:

• Stage 1 - Fast Screening: Rapid binary classification (crime/normal) in ~2ms, allowing most normal footage to be processed with minimal latency
• Stage 2 - Detailed Analysis: Flagged videos undergo multi-class classification to identify specific crime types
• Temporal Analysis: Analyzes motion patterns across multiple frames to detect suspicious activity
• Hotspot Detection: Returns spatial coordinates of detected abnormal activity regions

Response Structure

The API returns a structured JSON response containing:

• label: String - classification label ("normal" or "abnormal_or_crime")
• alert: Boolean - true if abnormal or criminal activity detected
• abnormality_probability: Float (0.0-1.0) - probability of abnormality or crime
• confidence: Float (0.0-1.0) - model's confidence in the classification
• num_frames: Integer - number of video frames analyzed
• hotspots: Array - spatial coordinates (x, y, intensity) of detected activity regions

Example Response:

{
  "label": "abnormal_or_crime",
  "alert": true,
  "abnormality_probability": 0.87,
  "confidence": 0.92,
  "num_frames": 16,
  "hotspots": [
    { "x": 0.45, "y": 0.62, "intensity": 0.89 },
    { "x": 0.32, "y": 0.71, "intensity": 0.67 }
  ]
}
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Detected Activities

The model is trained to detect 17 categories of abnormal and criminal activities:

Violent Crimes

• Abuse: Physical abuse and mistreatment
• Assault: Physical attacks on individuals
• Fighting: Physical altercations between multiple people
• Violence: General violent behavior and aggression
• Shooting: Firearm-related incidents

Property Crimes

• Burglary: Breaking and entering into buildings
• Robbery: Theft with force or threat
• Stealing: Theft of property
• Shoplifting: Retail theft
• Vandalism: Intentional property damage
• Arson: Deliberate fire-setting

Public Safety Incidents

• Explosion: Explosive incidents and blasts
• Road Accidents: Vehicle collisions and traffic incidents
• Accident: General accidents and injuries

Security Breaches

• Fence Climbing: Unauthorized perimeter breach attempts
• Weaponized: Presence of weapons or armed individuals
• Arrest: Law enforcement arrest situations

Performance Metrics

Metric	Value
F1 Score	91.6%
Recall (Sensitivity)	94.3%
Precision	89.0%
AUC-ROC	91.6%
Accuracy	86.9%
Average Response Time	2-8ms (depending on content)
Max File Size	100MB
Supported Formats	MP4, MOV, AVI, WebM, MKV

Deployment Options

This model supports flexible deployment configurations to match your infrastructure needs:

Cloud API (Default)

Send video files or URLs directly to our cloud API for processing. Ideal for batch analysis of recorded footage and applications with moderate throughput requirements.

• Batch Processing: Upload stored video files or provide URLs to recorded footage for analysis
• Frame Extraction: Submit batches of extracted frames from stored videos for efficient processing
• Webhook Notifications: Receive alerts when analysis completes via configured webhooks

On-Premise Docker (Real-time)

Deploy the model locally via Docker for real-time streaming analysis. Required for live CCTV monitoring with minimal latency.

• RTP/RTSP Streams: Connect directly to live camera feeds via RTP/RTSP protocols
• Low Latency: Process frames locally with 2-8ms inference time per batch
• Multi-Camera Support: Monitor multiple camera feeds simultaneously on a single GPU
• Air-Gapped Networks: Operate in secure environments without internet connectivity

Contact sales for on-premise deployment licensing and Docker image access.

Use Cases

• Retail Security: Monitor store footage for theft, shoplifting, and violence
• Public Safety: Analyze public space surveillance for criminal activity and accidents
• Corporate Security: Monitor office and facility access points for unauthorized entry
• Transportation Hubs: Secure airports, train stations, and bus terminals
• Banking: ATM and branch security monitoring for robberies
• Perimeter Security: Detect fence climbing and unauthorized access attempts
• Traffic Monitoring: Detect road accidents for rapid emergency response

Known Limitations

Important Considerations:

• Video Quality: Low resolution or poorly lit footage may reduce accuracy
• Camera Angle: Model trained primarily on fixed CCTV angles; unusual perspectives may affect detection
• Crowded Scenes: Very dense crowds may be challenging to analyze accurately
• Context Dependent: Some normal activities (e.g., sports, stunts) may appear suspicious without context
• False Positive Rate: Approximately 11% of normal videos may trigger alerts

Disclaimers

This model provides probability scores, not definitive determinations of criminal activity.

• Screening Tool: Use as part of a comprehensive security strategy with human oversight
• Not Legal Evidence: Detection results indicate probability and should not be used as sole legal evidence
• Human Review Required: All flagged incidents should be reviewed by trained security personnel
• Privacy Compliance: Ensure usage complies with local privacy laws and regulations (GDPR, CCPA, etc.)

Best Practice: Use this model as an alert system to direct human attention to potential incidents. All security decisions should involve human judgment and follow established protocols.