🎥 Criminal Activity Detection Model Card

Overview

Intended Use

✅ Primary Use Cases

• Surveillance monitoring (CCTV analysis)
• Smart city safety systems
• Automated alerting for abnormal activities
• Pre-screening large volumes of video footage

⚠️ Limitations / Out-of-Scope

• Not suitable for legal or forensic decision-making without human review
• Not intended for identifying individuals or facial recognition
• Performance may degrade under:
  • Low-light conditions
  • Unusual camera angles
  • Domains outside the training distribution

Model Architecture

Input Video (2-second clip, 16 frames)
             ↓
ResNet50 (ImageNet pretrained, frozen)
             ↓
Frame Embeddings (16 × 2048)
             ↓
LayerNorm → Dense(256) → Dropout(0.4)
             ↓
Bidirectional GRU (64 units)
             ↓
Temporal Attention Layer
             ↓
Dense(64) → Dropout(0.6)
             ↓
Sigmoid Output → P(Suspicious / Normal)

Key Design Choices

• Frozen CNN backbone → reduces overfitting on limited data
• Bidirectional GRU → captures forward and backward temporal dependencies
• Attention mechanism → emphasizes important frames within each clip

Training Details

Training Strategy

• Precomputed CNN features cached for efficiency
• Stratified video-level splits to prevent data leakage
• Decision threshold tuned using macro-F1 score

Dataset

Source

• Daudshah/Video-dataset
• ~7 GB worth of 20-30 seconds of surveillance video clips

Classes

• Suspicious (13 categories): Abuse, Arrest, Arson, Assault, Burglary, Explosion, Fighting, Road Accident, Robbery, Shooting, Shoplifting, Stealing, Vandalism
• Normal: Non-criminal surveillance footage

Data Splits

Performance

Class-wise Performance

Error Analysis

• False Positives: 12 / 150
• False Negatives: 16 / 140

Inference

Supported Inputs

• Video files (MP4, AVI, etc.)
• Live streams (Webcam, RTSP, RTMP)

Inference Method

• Sliding window approach over video streams
• Default 50% overlap to reduce missed detections

Performance

• ~135 ms per 2-second clip (NVIDIA T4 GPU)
• Real-time capable with batching and warm-up

Known Issues & Mitigations

Ethical Considerations

• False positives may trigger unnecessary alerts
• False negatives may miss critical incidents
• Should always be used with human oversight
• Avoid deployment in sensitive or high-risk scenarios without proper bias evaluation

Future Work

• Fine-tune CNN backbone with regularization
• Extend to multi-class classification (crime type detection)
• Explore transformer-based temporal models
• Optimize for edge deployment (TensorRT / ONNX)
• Improve domain adaptation for real-world CCTV environments