Video-Driven Data Generation Pipeline

• A video-driven data generation pipeline is a structured system that transforms raw videos into high-quality, annotated datasets using automated filtering and tagging methods.
• It employs sequential stages including frame extraction, quality filtering via the variance-of-Laplacian, and redundancy removal using SSIM metrics to optimize data retention.
• The pipeline integrates advanced object detection and version control with scalable MLOps deployment, ensuring reproducibility and adaptability across various domains.

A video-driven data generation pipeline is a structured, automated system that transforms raw video sequences into curated, annotated, and application-specific datasets for downstream machine learning workflows. These pipelines integrate quality filtering, content deduplication, object detection, scene tagging, and persistent versioned export to enable scalable development, deployment, and monitoring of computer vision and related models. The core purpose is to reduce manual effort, maximize information density, and guarantee reproducibility in ML data provisioning under efficiency and latency constraints (Roychowdhury et al., 2021).

1. Pipeline Architecture and Data Flow

The canonical video-driven data pipeline comprises discrete, parallelizable stages from raw video ingestion to persistent dataset output:

• Inputs: Raw video files or directories, each at a defined frame rate $F$ (frames/sec) and pixel resolution $W \times H$.
• Stage 1: Frame Extraction: Decode the video into individual images $I_1, \ldots, I_N$ with recorded indices and timestamps.
• Stage 2: Image Quality Filtering: Apply the variance-of-Laplacian operator to grayscale frames. Compute $Q_{VOL}(I_i) = \operatorname{Var}(\nabla^2 G(I_i))$. Discard frames with $Q_{VOL}(I_i) < v$, with $v$ user-tunable (typ. $300 \leq v \leq 1000$).
• Stage 3: Content Redundancy Filtering: Use Structural Similarity Index (SSIM) to remove visually redundant frames. For candidate frame $I_i$, compare to last retained frame $I_j$:

$$SSIM(I_i,I_j) = \frac{(2\mu_{I_i}\mu_{I_j} + C_1)(2\sigma_{I_i I_j} + C_2)}{(\mu_{I_i}^2 + \mu_{I_j}^2 + C_1)(\sigma_{I_i}^2 + \sigma_{I_j}^2 + C_2)}$$

Discard $I_i$ if $SSIM(I_i,I_j) > s$, $s \in [0, 1]$.

• Stage 4: Automated Object Detection & Tagging: Apply object detectors (e.g., Faster-RCNN, Mask-RCNN). Detections with confidence $\geq p_0$ populate class counts and frame tags. Apply Boolean rules to assign scene labels such as 'City', 'Freeway', etc.
• Stage 5: Metadata and Persistence: For each retained frame, emit a YAML record with image metrics, detection lists, scene tags, and provenance. Export sequence-level and dataset-wide manifests for version tracking.
• Stage 6: Model-Serving and Monitoring: Containerize detection and pipeline steps (e.g., Docker, Kubernetes), deploy distributedly, and integrate instrumented metrics (Prometheus, Grafana) for real-time monitoring and rollback.

The entire flow is computationally efficient: for $\sim$6500 frames (KITTI sequences), all steps (quality filtering, redundancy, detection) run in under 30 seconds on 16-core CPUs and a single GPU (Roychowdhury et al., 2021).

2. Key-Frame Selection and Retention Control

Video-driven pipelines are designed to select an informative subset of all frames, balancing information density against storage, labeling, or compute costs.

• Quality Score (VOL): $Q_{VOL}(I) = \operatorname{Var}(\nabla^2 G(I))$. Empirical thresholds: $v \simeq 300$ keeps most frames; $v \geq 800$ yields 10–20% retention.
• Content Novelty (SSIM): $s = 0.2$ yields highly aggressive de-duplication (retaining only 10–25%); $s = 0.7$ yields moderate (30–50%).
• Retention Calibration: Let $$S(v,s) = \{I_i: Q_{VOL}(I_i)\geq v \wedge SSIM(I_i,I_{\textrm{last}})\leq s\}$$, so retention $R(v,s) = |S(v,s)| / N_{\textrm{total}}$. Sweeping over $v$ and $s$ on a calibration subset controls final retention fraction (target 0.1–20%) (Roychowdhury et al., 2021).

These heuristics are domain-adaptable: for highly dynamic or low-quality domains (e.g., CCTV), filter parameters are raised to control for flicker and redundancy.

3. Automated Tagging, Annotation, and Scene Semantics

Object detection and metadata generation are core to downstream automation and dataset lineage tracking:

• Object Detectors: Pre-trained detectors are run on the retained frames, with low detection thresholds (e.g., $p_0=0.1$) to increase recall.
• Scene Labeling: Rule sets assign frames to semantic classes based on detected object group counts, e.g., 'City' if an UrbanVehicle is present or if both Vehicles and People occur; 'Freeway' for $\geq$2 Vehicles without People (Roychowdhury et al., 2021).
• Metadata Format: Detailed YAML per frame encapsulates frame ids, image scores, object detections and probabilities, group counts, scene label, and reason for retention or discard.
• Sequence Manifest: Aggregates counts for each removal reason and per-class statistics, ensuring transparent versioned deployment and facilitating data drift detection.

This structure supports MLOps requirements such as automated data retrieval, model versioning, and auditability.

4. Computational Efficiency and Scalability

• Operational Costs (per frame): VOL $\approx$1.5 ms (CPU), SSIM $\approx$5 ms (CPU), object detection 150–700 ms (GPU, depending on architecture).
• Scaling: All core steps (VOL, SSIM, object detection) are parallelizable. Detection is $O(MC)$ per frame ($M$ = proposals, $C$ = CNN cost) (Roychowdhury et al., 2021).
• Deployment: Pipelines are Dockerized, deployed on Kubernetes (2–20 pods/horizontal pod autoscaling), and monitored for mean batch latency and detection confidence. Instrumentation using Prometheus and Grafana enables threshold-based automated rollbacks or canary deployments.
• Adaptability: Pipeline parameters are easily tunable for new domains (CCTV, medical, satellite), substituting quality and novelty metrics or object groupings as required.

5. Extensibility to Multiple Domains

Video-driven pipelines are not confined to standard driving or surveillance contexts; modifications are recommended for broader applicability:

• Surveillance (CCTV): For higher frame rates, $s$ is increased to avoid over-filtering. Noise due to lighting flicker leads to $v \geq 800$.
• Medical Video: When variance-of-Laplacian does not reflect clinical quality, learned frame-level scores (e.g., contrastive encoders) are substituted. SSIM filtering may use patch-level comparisons.
• Aerial/Satellite: Edge-density or Laplacian-of-Gaussian can replace pure Laplacian; redundancy filtering can employ blockwise SSIM or NDVI-channel histogram intersection (Roychowdhury et al., 2021).

A plausible implication is that similar adaptation is feasible in domains such as video-based quality control in manufacturing or micro-gesture analysis, provided domain-specific feature selection is implemented.

6. Version Control, Monitoring, and MLOps Integration

Persistent labeling and tracking are fundamental for robust, reproducible machine learning:

• Export Format: Retained frames and metadata are versioned (semantic versioning), with sequence manifests containing timing and summary statistics.
• Pipeline Integration: The exported data artifacts are consumable by MLOps orchestration tools (Kubeflow, ArgoCD).
• Runtime Monitoring: Real-time tracking of error rates, latency, and detection confidences facilitates adaptive deployments (blue/green or canary) and rapid rollback in response to drift or anomalies.

These practices ensure traceable lineage from raw input to deployed model iteration, supporting continuous delivery (CD) and continuous integration (CI) in production ML workflows.

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By integrating these mechanisms—frame selection by image quality and content diversity, domain-tuned object detection and tagging, scalable orchestration, detailed and versioned metadata, and MLOps-ready monitoring—a video-driven data generation pipeline enables automated, reproducible, and efficient provisioning of video datasets for machine learning applications (Roychowdhury et al., 2021).