Spotting the Unusual: Video Anomaly Detection with Factorized Self-Attention Transformers

Executive Summary

In the rapidly evolving landscape of intelligent surveillance systems, the ability to automatically detect anomalous events in video streams has become paramount for public safety and security applications. This article presents a groundbreaking approach using Factorized Self-Attention (FSA) Transformers for video anomaly detection, achieving an unprecedented 91.7% Average Precision on the challenging XD-Violence benchmark dataset.

Our method addresses the fundamental challenges of video anomaly detection through innovative architectural design: factorizing attention mechanisms to efficiently process long video sequences, integrating multi-modal learning with audio-visual fusion, and leveraging weak supervision to reduce annotation costs. The result is a deployable system that can identify violence, accidents, and other anomalous events in real-time surveillance footage using only video-level labels during training.

Key Achievement: By decomposing self-attention into spatial and temporal components and fusing audio-visual cues through cross-modal interaction, we surpassed the previous state-of-the-art by 6 percentage points while maintaining computational efficiency suitable for real-world deployment.

The Challenge: Why Video Anomaly Detection Remains Unsolved

Video anomaly detection represents one of the most challenging problems in computer vision, sitting at the intersection of temporal modeling, rare event detection, and real-world deployment constraints. The complexity stems from several fundamental challenges that traditional approaches have struggled to address effectively.

The Rarity Problem

Anomalous events in surveillance footage are inherently rare, often comprising less than 1% of total video content. This extreme class imbalance creates significant challenges for traditional supervised learning approaches:

• Limited Training Data: Anomalous events are scarce, making it difficult to collect sufficient training examples
• Class Imbalance: Normal activities vastly outnumber anomalous ones, leading to biased models
• Annotation Costs: Frame-level annotation of anomalous events requires expert knowledge and is prohibitively expensive

Temporal Complexity

Unlike static image analysis, video anomaly detection requires understanding complex temporal patterns:

• Variable Duration: Anomalous events can span from seconds to minutes
• Temporal Context: The same action might be normal or anomalous depending on context
• Long-Range Dependencies: Critical information may be separated by long temporal distances

Visual Diversity and Ambiguity

Anomalous events exhibit tremendous visual diversity while normal events can appear similar to anomalous ones:

• Intra-Class Variation: Different types of violence (fights, shootings, explosions) have vastly different visual signatures
• Inter-Class Similarity: Some normal activities (sports, celebrations) can appear violent without context
• Environmental Factors: Lighting, camera angles, and resolution affect detection accuracy

Computational Constraints

Real-world deployment requires balancing accuracy with computational efficiency:

• Real-Time Processing: Surveillance systems need near-instantaneous anomaly detection
• Resource Limitations: Edge devices have limited computational resources
• Scalability: Systems must handle multiple concurrent video streams

Our approach addresses these challenges through innovative architectural design and training strategies that we'll explore in detail.

Factorized Self-Attention: Rethinking Video Understanding

The core innovation of our approach lies in the Factorized Self-Attention (FSA) mechanism, which fundamentally reimagines how Transformers process video data. Traditional video transformers suffer from quadratic complexity when applied to the spatiotemporal domain, making them impractical for long video sequences typical in surveillance applications.

The Problem with Standard Video Transformers

Standard approaches to video understanding with Transformers typically flatten spatiotemporal data into a single sequence of tokens:

Video (T×H×W×C) → Tokens (T×H×W, C) → Self-Attention O((THW)²)

This approach leads to prohibitive computational costs:

• Memory Requirements: Quadratic growth in memory usage with video length
• Computational Complexity: O(n²) attention computation where n = T×H×W
• Limited Context: Forced to use short clips or low resolution to maintain feasibility

Factorized Self-Attention Architecture

Our FSA mechanism decomposes the spatiotemporal attention into two sequential operations:

1. Spatial Attention Within Frames:

For each frame t: X_t → SpatialAttention(X_t) → Y_t
Complexity: T × O((HW)²)

2. Temporal Attention Across Frames:

Y = [Y_1, Y_2, ..., Y_T] → TemporalAttention(Y) → Z
Complexity: O(T²)

Total Complexity Reduction:

• Standard: O((THW)²) = O(T²H²W²)
• FSA: O(T×(HW)² + T²) ≈ O(T×H²W²) when T << HW

This factorization provides several key advantages:

Computational Efficiency

The separation of spatial and temporal attention dramatically reduces computational complexity:

• Linear Scaling: Memory usage scales linearly with video length rather than quadratically
• Parallelization: Spatial attention within frames can be computed in parallel
• Hardware Friendly: Better cache locality and memory access patterns

Semantic Interpretability

The factorized design aligns with the natural structure of video data:

• Spatial Reasoning: Within-frame attention captures object interactions and scene understanding
• Temporal Reasoning: Cross-frame attention models motion patterns and temporal dependencies
• Hierarchical Understanding: Builds video understanding from frame-level to sequence-level features

Enhanced Modeling Capacity

Despite computational savings, FSA maintains strong modeling capacity:

• Global Context: Still captures long-range spatiotemporal dependencies
• Flexible Architecture: Can be easily integrated into existing Transformer architectures
• Scalable Design: Handles variable-length video sequences efficiently

Multi-Modal Architecture: Beyond Visual Information

Real-world anomaly detection benefits significantly from multi-modal information, particularly the integration of audio cues that often accompany anomalous events. Our architecture incorporates a sophisticated audio-visual fusion mechanism that leverages both modalities for enhanced detection performance.

Audio Processing Pipeline

Audio Spectrogram Transformer (AST) Integration:

Our audio processing pipeline is built around the Audio Spectrogram Transformer, specifically designed to capture acoustic signatures of anomalous events:

1. Audio Preprocessing:

• Sampling Rate: 16 kHz for optimal frequency resolution
• Window Size: 25ms Hamming windows with 10ms hop length
• Mel-Scale Conversion: 128 mel-frequency bins for perceptual accuracy
• Log-Mel Features: Logarithmic compression for dynamic range handling

2. Temporal Segmentation:

• Synchronization: Audio segments aligned with video frame sequences
• Context Window: 10-second audio clips for temporal context
• Overlap Strategy: 50% overlap between consecutive segments

3. Feature Extraction:

Audio Waveform → STFT → Mel-Spectrogram → Log-Compression → AST → Audio Features

Cross-Modal Interaction (CMI) Mechanism

The Cross-Modal Interaction mechanism enables sophisticated fusion of audio and visual information through learnable attention mechanisms:

Architecture Design:

Video Features (V) ∈ R^(T×d_v)
Audio Features (A) ∈ R^(T×d_a)

Video-to-Audio Attention: V' = CrossAttention(Q=V, K=A, V=A)
Audio-to-Video Attention: A' = CrossAttention(Q=A, K=V, V=V)

Fused Features: F = Concat([V', A']) ∈ R^(T×(d_v+d_a))

Key Components:

1. Bidirectional Cross-Attention: Both modalities inform each other through cross-attention mechanisms
2. Temporal Alignment: Ensures proper synchronization between audio and visual features
3. Adaptive Weighting: Learns to emphasize the most informative modality for each temporal segment
4. Residual Connections: Preserves original modality information while adding cross-modal enhancements

Why Multi-Modal Matters for Anomaly Detection

Audio Signatures of Anomalous Events:

• Violence: Screaming, shouting, aggressive vocalizations
• Accidents: Breaking glass, metal impact, vehicle collisions
• Explosions: Distinctive acoustic signatures and aftermath sounds
• Crowd Dynamics: Panic, stampedes, unusual crowd vocalizations

Complementary Information:

• Occlusion Robustness: Audio continues when visual information is blocked
• Distance Independence: Audio travels further than detailed visual information
• Temporal Precision: Audio events often have sharper temporal boundaries
• Context Enhancement: Audio provides semantic context for ambiguous visual scenes

Dataset and Experimental Methodology

XD-Violence: A Comprehensive Benchmark

Our primary evaluation utilizes the XD-Violence dataset, one of the most challenging and comprehensive benchmarks for video anomaly detection:

Violence Categories:

• Physical Violence: Fighting, assault, domestic violence
• Armed Violence: Shootings, stabbings, weapon-based attacks
• Crowd Violence: Riots, stampedes, mob activities
• Explosive Violence: Bombings, explosions, destruction
• Vehicle Violence: Car accidents, vehicle-based attacks
• Property Violence: Vandalism, arson, property destruction

Dataset Characteristics:

• Real-World Footage: Collected from surveillance cameras, news broadcasts, and social media
• Diverse Environments: Indoor/outdoor scenes, various lighting conditions, multiple camera angles
• Temporal Complexity: Events ranging from 2 seconds to several minutes
• Quality Variation: Different resolutions and compression levels mimicking real deployment scenarios

Training Methodology

Weak Supervision Framework:

Our training approach leverages Robust Temporal Feature Magnitude (RTFM) learning, specifically designed for weak supervision scenarios:

1. Multiple Instance Learning (MIL) Formulation:

• Positive Bags: Videos containing anomalous events (location unknown)
• Negative Bags: Videos with only normal activities
• Instance Ranking: Learn to rank temporal segments within positive bags

2. Feature Magnitude Learning:

# Conceptual RTFM loss formulation
def rtfm_loss(features, video_labels):
    # Compute feature magnitudes
    magnitudes = torch.norm(features, dim=-1)
    
    # Rank segments by magnitude
    ranked_segments = torch.argsort(magnitudes, descending=True)
    
    # Top-k segments for positive videos should have high scores
    # Bottom-k segments for negative videos should have low scores
    loss = ranking_loss(ranked_segments, video_labels)
    return loss

3. Training Configuration:

• Optimizer: AdamW with cosine annealing schedule
• Learning Rate: 1e-4 with 1000-step warmup
• Batch Size: 16 videos per batch (hardware dependent)
• Clip Length: 32 frames (≈1 second at 30 FPS)
• Training Duration: 100 epochs with early stopping

Data Augmentation Strategy:

• Temporal Augmentation: Random temporal cropping, speed variation
• Spatial Augmentation: Random cropping, horizontal flipping, color jittering
• Audio Augmentation: Time stretching, pitch shifting, noise injection
• Cross-Modal Augmentation: Temporal offset between audio and video for robustness

Architecture Implementation Details

Our end-to-end architecture integrates multiple sophisticated components into a cohesive anomaly detection system:

Figure: Complete architecture showing the flow from input video through FSA Video Transformer, Audio Spectrogram Transformer, Cross-Modal Interaction, to final anomaly scoring via RTFM head.

Video Processing Pipeline

1. Frame Extraction and Preprocessing:

# Conceptual video preprocessing pipeline
def preprocess_video(video_path):
    frames = extract_frames(video_path, target_fps=30)
    frames = resize_frames(frames, size=(224, 224))
    frames = normalize_frames(frames, mean=[0.485, 0.456, 0.406], 
                             std=[0.229, 0.224, 0.225])
    clips = create_clips(frames, clip_length=32, overlap=0.5)
    return clips

2. FSA Video Transformer Configuration:

• Input Resolution: 224×224 pixels per frame
• Patch Size: 16×16 pixels (196 patches per frame)
• Embedding Dimension: 768
• Number of Layers: 12 transformer blocks
• Attention Heads: 12 heads for spatial attention, 8 heads for temporal attention
• Position Encoding: 3D sinusoidal encoding for spatiotemporal positions

3. Spatial Attention Implementation:

class SpatialAttention(nn.Module):
    def __init__(self, dim, num_heads):
        super().__init__()
        self.num_heads = num_heads
        self.scale = (dim // num_heads) ** -0.5
        self.qkv = nn.Linear(dim, dim * 3)
        self.proj = nn.Linear(dim, dim)
    
    def forward(self, x):
        # x: (batch, time, height*width, dim)
        B, T, N, C = x.shape
        x = x.reshape(B*T, N, C)  # Process each frame independently
        
        qkv = self.qkv(x).reshape(B*T, N, 3, self.num_heads, C//self.num_heads)
        q, k, v = qkv.permute(2, 0, 3, 1, 4)  # (3, B*T, heads, N, dim)
        
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        
        x = (attn @ v).transpose(1, 2).reshape(B*T, N, C)
        x = self.proj(x)
        return x.reshape(B, T, N, C)

Audio Processing Implementation

1. Audio Spectrogram Transformer (AST) Configuration:

• Input Features: 128 mel-frequency bins
• Temporal Patches: 16×16 time-frequency patches
• Model Size: AST-Base (86M parameters)
• Pre-training: ImageNet-21K initialized weights adapted for audio
• Fine-tuning: Task-specific adaptation on XD-Violence audio

2. Audio Feature Extraction:

class AudioProcessor:
    def __init__(self):
        self.ast = ASTModel.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593')
        self.mel_transform = torchaudio.transforms.MelSpectrogram(
            sample_rate=16000, n_mels=128, hop_length=160
        )
    
    def extract_features(self, audio_waveform):
        # Convert to mel-spectrogram
        mel_spec = self.mel_transform(audio_waveform)
        log_mel = torch.log(mel_spec + 1e-7)
        
        # Extract AST features
        audio_features = self.ast(log_mel)
        return audio_features

Cross-Modal Interaction Details

Implementation of Bidirectional Cross-Attention:

class CrossModalInteraction(nn.Module):
    def __init__(self, video_dim, audio_dim, hidden_dim):
        super().__init__()
        self.video_to_audio = CrossAttentionBlock(video_dim, audio_dim, hidden_dim)
        self.audio_to_video = CrossAttentionBlock(audio_dim, video_dim, hidden_dim)
        self.fusion_layer = nn.Linear(video_dim + audio_dim, hidden_dim)
    
    def forward(self, video_features, audio_features):
        # Cross-modal attention
        video_enhanced = self.video_to_audio(video_features, audio_features)
        audio_enhanced = self.audio_to_video(audio_features, video_features)
        
        # Concatenate and fuse
        fused_features = torch.cat([video_enhanced, audio_enhanced], dim=-1)
        output = self.fusion_layer(fused_features)
        return output

Results and Performance Analysis

Our FSA-based approach achieved groundbreaking performance on the XD-Violence benchmark, setting a new state-of-the-art with significant improvements over existing methods:

Quantitative Results

Performance Analysis

Figure: Precision-Recall curve demonstrating consistent high precision across all recall levels, with an average precision of 0.92, indicating robust performance across different threshold settings.

Key Performance Insights:

1. Exceptional Average Precision (91.7%)

• 6-point improvement over previous state-of-the-art
• Indicates both high precision and recall across all anomaly types
• Demonstrates robust performance across diverse violence categories

2. Precision-Recall Characteristics:

• High Precision Maintenance: Precision remains above 0.90 across most recall levels
• Balanced Performance: No significant precision drop even at high recall
• Class Balance Robustness: Strong performance despite dataset imbalance

3. Ablation Study Results:

Computational Performance

Efficiency Metrics:

• Processing Speed: 45 FPS on RTX 3080 GPU
• Memory Usage: 8GB VRAM for 32-frame clips
• Model Size: 124M parameters (deployable on edge devices)
• Latency: <150ms end-to-end processing time

Scalability Analysis:

• Linear Scaling: Memory usage scales linearly with video length
• Batch Processing: Efficient batch processing of multiple video streams
• Hardware Compatibility: Runs on consumer-grade GPUs

Per-Category Performance

Category-Specific Insights:

• Best Performance: Explosive and Physical Violence (distinctive signatures)
• Challenging Categories: Crowd and Property Violence (visual ambiguity)
• Audio Contribution: Particularly strong for Armed and Explosive Violence

Technical Innovation: What Makes It Work

Factorized Attention Advantages

1. Computational Efficiency: The key breakthrough lies in the mathematical decomposition of attention complexity:

Standard Video Attention: O((T×H×W)²)
Factorized Attention: O(T×(H×W)² + T²)

For typical values (T=32, H=14, W=14):
Standard: O(175,616²) ≈ O(30.8B operations)
Factorized: O(32×196² + 32²) ≈ O(1.2M operations)

This represents a ~25,000x reduction in computational complexity while maintaining global context.

2. Semantic Alignment: The factorization naturally aligns with video understanding:

• Spatial Attention: Captures object interactions within frames
• Temporal Attention: Models motion patterns and event progression
• Hierarchical Processing: Builds understanding from local to global context

Multi-Modal Fusion Benefits

1. Complementary Information:

• Visual Occlusion Robustness: Audio continues when visual information is blocked
• Temporal Precision: Audio events often have sharper temporal boundaries
• Semantic Enhancement: Audio provides context for visually ambiguous scenes

2. Cross-Modal Attention Mechanism: The bidirectional cross-attention allows:

• Audio-Guided Visual Attention: Sound directs visual focus to relevant regions
• Visual-Informed Audio Processing: Visual context helps interpret acoustic events
• Adaptive Fusion: Learns optimal combination strategies for different scenarios

Weak Supervision Effectiveness

1. RTFM Learning Principle: The core insight is that anomalous segments exhibit higher feature magnitudes:

Normal segments: Low feature activation
Anomalous segments: High feature activation

2. Ranking-Based Learning: Instead of absolute classification, the model learns relative ranking:

• Within-Video Ranking: Identifies most anomalous segments within each video
• Cross-Video Consistency: Maintains consistent ranking across different videos
• Threshold Independence: Performance robust to different decision thresholds

Real-World Deployment Considerations

System Architecture for Production

1. Edge Computing Integration:

Camera Feed → Frame Buffer → FSA Processing → Anomaly Detection → Alert System

Hardware Requirements:

• Minimum: NVIDIA Jetson Xavier NX (edge deployment)
• Recommended: RTX 3060 or equivalent (server deployment)
• Memory: 8GB minimum, 16GB recommended
• Storage: 500GB SSD for model and buffer storage

2. Streaming Pipeline:

class RealTimeAnomalyDetector:
    def __init__(self):
        self.model = load_fsa_model()
        self.frame_buffer = FrameBuffer(max_size=1000)
        self.audio_buffer = AudioBuffer(max_size=160000)  # 10 seconds at 16kHz
        
    async def process_stream(self, video_stream, audio_stream):
        while True:
            # Buffer frames and audio
            frames = await self.frame_buffer.get_batch(32)
            audio = await self.audio_buffer.get_segment(10.0)
            
            # Process with FSA model
            anomaly_score = self.model.predict(frames, audio)
            
            # Trigger alerts if threshold exceeded
            if anomaly_score > self.threshold:
                await self.send_alert(anomaly_score, frames[-1])

Scalability and Performance

1. Multi-Camera Support:

• Parallel Processing: Independent streams processed simultaneously
• Load Balancing: Dynamic allocation of computational resources
• Priority Queuing: Critical cameras get processing priority

2. Alert Management:

class AlertSystem:
    def __init__(self):
        self.severity_levels = {
            'low': (0.7, 0.8),
            'medium': (0.8, 0.9),
            'high': (0.9, 1.0)
        }
    
    async def process_alert(self, score, video_segment, metadata):
        severity = self.determine_severity(score)
        
        # Generate alert with context
        alert = {
            'timestamp': metadata['timestamp'],
            'camera_id': metadata['camera_id'],
            'severity': severity,
            'confidence': score,
            'video_clip': video_segment,
            'description': self.generate_description(score, metadata)
        }
        
        await self.dispatch_alert(alert)

Integration with Existing Systems

1. Security Management Platforms:

• ONVIF Compatibility: Standard protocol for IP cameras
• REST API: Integration with existing security software
• Database Integration: Storage of alerts and metadata
• Dashboard Integration: Real-time monitoring interfaces

2. Privacy and Compliance:

• Local Processing: No video data leaves premises
• Selective Recording: Only anomalous segments stored
• Access Control: Role-based access to system functions
• Audit Trails: Complete logging of system activities

Limitations and Future Directions

Current Limitations

1. Dataset Bias and Generalization:

• Training Distribution: Performance may degrade on significantly different environments
• Cultural Context: Violence definitions vary across cultures and contexts
• Edge Cases: Rare anomaly types not well represented in training data

2. False Positive Management:

• Context Sensitivity: Some normal activities may appear anomalous without context
• Environmental Factors: Lighting, weather, and camera quality affect performance
• Temporal Boundaries: Precise start/end detection of anomalous events remains challenging

3. Computational Requirements:

• Hardware Dependency: Still requires dedicated GPU hardware for real-time processing
• Power Consumption: Significant power requirements for continuous operation
• Scalability Limits: Performance may degrade with too many concurrent streams

Future Research Directions

1. Enhanced Temporal Modeling:

• Hierarchical Temporal Attention: Multi-scale temporal understanding
• Causal Modeling: Understanding cause-effect relationships in anomalous events
• Long-Range Dependencies: Better modeling of events spanning multiple minutes

2. Advanced Multi-Modal Integration:

• Additional Modalities: Integration of thermal imaging, depth information
• Contextual Information: Incorporation of metadata (time, location, weather)
• Social Context: Understanding crowd dynamics and social interactions

3. Continual Learning and Adaptation:

• Online Learning: Adaptation to new environments without retraining
• Few-Shot Learning: Quick adaptation to new anomaly types
• Domain Adaptation: Transfer learning across different surveillance scenarios

4. Explainability and Trust:

• Attention Visualization: Clear indication of what the model focuses on
• Confidence Calibration: Better uncertainty quantification
• Human-in-the-Loop: Seamless integration of human oversight

Emerging Applications

1. Smart City Infrastructure:

• Traffic Monitoring: Accident detection and traffic flow analysis
• Public Safety: Crowd monitoring and emergency response
• Infrastructure Protection: Monitoring of critical facilities

2. Industrial Safety:

• Workplace Safety: Detection of safety violations and accidents
• Equipment Monitoring: Anomaly detection in industrial processes
• Environmental Monitoring: Detection of environmental hazards

3. Healthcare and Eldercare:

• Patient Monitoring: Fall detection and medical emergencies
• Behavioral Analysis: Monitoring of patient behavior patterns
• Assisted Living: Safety monitoring for elderly residents

Broader Impact and Ethical Considerations

Societal Benefits

1. Public Safety Enhancement:

• Rapid Response: Faster emergency response times through automated detection
• Crime Prevention: Deterrent effect of automated surveillance systems
• Resource Optimization: More efficient allocation of security personnel

2. Cost Reduction:

• Reduced Manual Monitoring: Decreased need for human surveillance operators
• Preventive Measures: Early detection prevents escalation of incidents
• Insurance Benefits: Reduced liability through better incident documentation

Ethical Considerations and Responsible Deployment

1. Privacy Protection:

• Minimal Data Collection: Only necessary data should be processed and stored
• Local Processing: Video analysis should occur locally when possible
• Data Encryption: All data should be encrypted in transit and at rest
• Access Controls: Strict controls on who can access surveillance data

2. Bias and Fairness:

• Algorithmic Bias: Regular auditing for biased detection patterns
• Demographic Fairness: Ensuring equal performance across different populations
• Cultural Sensitivity: Adaptation to local cultural norms and definitions of anomalies

3. Transparency and Accountability:

• Explainable Decisions: Clear explanation of why alerts were triggered
• Human Oversight: Maintaining human involvement in critical decisions
• Audit Trails: Complete logging of system decisions and human interventions
• Regular Evaluation: Continuous assessment of system performance and fairness

Regulatory Compliance

1. Data Protection Regulations:

• GDPR Compliance: Adherence to European data protection standards
• Local Privacy Laws: Compliance with regional privacy regulations
• Consent Management: Appropriate consent mechanisms where required

2. Security Standards:

• Cybersecurity: Protection against unauthorized access and manipulation
• Data Integrity: Ensuring authenticity and integrity of surveillance data
• Backup and Recovery: Robust data backup and disaster recovery procedures

Conclusion

Our research demonstrates that Factorized Self-Attention Transformers represent a significant breakthrough in video anomaly detection, achieving unprecedented performance while maintaining practical deployment feasibility. The combination of innovative architectural design, multi-modal learning, and efficient training strategies has resulted in a system that surpasses previous state-of-the-art methods by a substantial margin.

Key Contributions

1. Architectural Innovation:

• FSA Mechanism: Successful decomposition of spatiotemporal attention for efficient video processing
• Multi-Modal Fusion: Effective integration of audio-visual information through cross-modal attention
• Scalable Design: Linear complexity scaling enabling real-world deployment

2. Performance Achievement:

• 91.7% Average Precision: New state-of-the-art on XD-Violence benchmark
• Computational Efficiency: Real-time processing capability on consumer hardware
• Robust Generalization: Strong performance across diverse anomaly categories

3. Practical Impact:

• Deployable System: Ready for real-world surveillance applications
• Cost-Effective Training: Weak supervision reduces annotation requirements
• Scalable Architecture: Supports multi-camera deployments

Research Impact and Future Outlook

This work establishes FSA as a fundamental technique for efficient video understanding, with implications extending beyond anomaly detection to general video analysis tasks. The successful integration of weak supervision with multi-modal learning provides a template for developing practical AI systems that balance performance with deployment constraints.

Future Developments: As video understanding continues to evolve, we anticipate further improvements through:

• Advanced Attention Mechanisms: More sophisticated factorization strategies
• Larger-Scale Training: Leveraging larger datasets and self-supervised learning
• Edge Computing Optimization: Further efficiency improvements for mobile deployment
• Cross-Domain Adaptation: Better generalization across different surveillance scenarios

Final Thoughts

The convergence of efficient Transformer architectures, multi-modal learning, and practical deployment considerations represents a significant step forward in making AI-powered surveillance systems both effective and deployable. Our work provides a foundation for next-generation security systems that can automatically detect and respond to anomalous events while respecting privacy, efficiency, and accuracy requirements.

The success of this approach underscores the importance of algorithm-hardware co-design in developing practical AI systems. By carefully considering computational constraints alongside performance requirements, we can create solutions that not only advance the state-of-the-art but also translate into real-world impact.

As we continue to refine and extend these techniques, the goal remains clear: developing AI systems that enhance public safety and security while maintaining the highest standards of privacy, fairness, and reliability. The foundation established by this work provides a solid platform for achieving these ambitious but essential objectives.

References

[1] R. Karthik, A. Srinivasan, P. Shalmiya, V. Subramaniyaswamy, "Video Anomaly Detection using Factorized Self-Attention Transformer," Proc. 2024 Int. Conf. on Computational Intelligence and Network Systems (CINS), IEEE, 2024. DOI: 10.1109/CINS63881.2024.10862995

[2] Y. Tian et al., "Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning," Proc. IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4975-4986.

[3] Y. Gong et al., "AST: Audio Spectrogram Transformer," Proc. Interspeech, 2021, pp. 571-575.

[4] W. Wu et al., "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision," Proc. European Conference on Computer Vision (ECCV), 2020, pp. 322-339.

[5] A. Vaswani et al., "Attention is All You Need," Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 5998-6008.