Human Extractor

Human Extractor Skill

Description

GPU-accelerated pipeline for detecting, tracking, and classifying humans in dashcam footage. Processes MP4 videos to extract human crops with optional CLIP-based head covering classification, saving all outputs to a unified directory with comprehensive indexing.

Purpose

Extract visual evidence of human presence from dashcam recordings for investigative analysis. Optimized for high throughput using NVDEC decoding, batched YOLOv8 detection, ByteTrack multi-object tracking, and optional CLIP classification.

Usage

Basic Invocation

Extract humans from Park_R videos on October 6, 2025

Advanced Invocation

Scan Park_R\20251006 and 20251007, keep only frames with people,
save all outputs in one folder, add one full-frame per timestamp with boxes,
use my GPU at max, filter for head-covered individuals at 80% confidence

Input Parameters

Required

• roots (list[str]): One or more source directories containing MP4 files
  • Example: ["G:\\My Drive\\PROJECTS\\INVESTIGATION\\DASHCAM\\Park_R\\20251006"]

Core Detection

• confidence (float, default: 0.35): YOLOv8 detection confidence threshold (0.0-1.0)
• iou (float, default: 0.50): IoU threshold for NMS (0.0-1.0)
• yolo_batch (int, default: 64): YOLOv8 batch size (32-128 depending on VRAM)

CLIP Filtering (Optional)

• clip_filter.enabled (bool, default: false): Enable head covering classification
• clip_filter.threshold (float, default: 0.80): CLIP confidence threshold
• clip_filter.batch (int, default: 384): CLIP batch size (256-512)

Hardware Acceleration

• nvdec (bool, default: true): Use NVIDIA hardware video decoding
• gpu_id (int, default: 0): CUDA device ID

Output Control

• single_output_dir (str, default: "parsed\ALL_CROPS"): Unified output directory
• save_full_frame (bool, default: false): Save one annotated full-frame per timestamp
• full_frame_maxw (int, default: 1280): Max width for full-frame saves
• draw_boxes (bool, default: true): Annotate boxes on full-frames

Filename Convention

• filename_version (str, default: "v1"): Version tag for output filenames

Deduplication

• dedup.enabled (bool, default: true): Enable similarity deduplication
• dedup.ssim (float, default: 0.92): SSIM threshold (0.0-1.0)
• dedup.rate_cap_per_track_per_min (int, default: 12): Max crops per track per minute

Parallel Processing

• parallel.dates (list[str], optional): Process multiple dates concurrently
• parallel.max_workers (int, default: 3): Max parallel date workers

Output Format

Success Response

{
  "status": "ok",
  "summary": {
    "videos_processed": 142,
    "crops_saved": 4414,
    "frames_saved": 728,
    "gpu_util_avg": 0.85,
    "processing_time_sec": 2847,
    "errors": 0
  },
  "artifacts": {
    "index_csv": "G:\\My Drive\\PROJECTS\\APPS\\Human_Detection\\parsed\\ALL_CROPS\\INDEX.csv",
    "output_dir": "G:\\My Drive\\PROJECTS\\APPS\\Human_Detection\\parsed\\ALL_CROPS",
    "log_file": "G:\\My Drive\\PROJECTS\\APPS\\Human_Detection\\parsed\\ALL_CROPS\\run_20251006_143022.log"
  },
  "performance": {
    "nvdec_active": true,
    "yolo_batch": 64,
    "clip_batch": 384,
    "avg_fps": 48.3,
    "vram_peak_gb": 9.2
  },
  "notes": [
    "NVDEC hardware decoding active",
    "Batched YOLO=64, CLIP=384",
    "GPU utilization: 85%"
  ]
}

Error Response

{
  "status": "error",
  "error": "CUDA out of memory",
  "suggestion": "Reduce batch sizes: yolo_batch=48, clip_batch=256",
  "partial_results": {
    "videos_processed": 67,
    "crops_saved": 2103
  }
}

Output Structure

Directory Layout

parsed\ALL_CROPS\
├── INDEX.csv                                    # Global master index
├── INDEX.20251006_pid1234.csv                   # Shard (pre-merge)
├── run_20251006_143022.log                      # Execution log
│
# Crop files (per person detection)
├── 20251006__20251006142644_070785B__t15234__f365__trk017__x1014y46w266h659__c85__v1.webp
├── 20251006__20251006143844_070787B__t8420__f202__trk003__x234y567w180h420__c92__v1.webp
│
# Full-frame files (optional, one per timestamp)
├── 20251006__20251006142644_070785B__t15234__FRAME__v1.webp
└── 20251006__20251006143844_070787B__t8420__FRAME__v1.webp

Filename Convention

Crop Format:

<date>__<video_stem>__t<ts_ms>__f<frame_idx>__trk<track_id>__x<x1>y<y1>w<w>h<h>__c<covered_0to100>__v<ver>.webp

Example:
20251006__20251006142644_070785B__t15234__f365__trk017__x1014y46w266h659__c85__v1.webp

Decoded:
- Date: 2025-10-06
- Video: 20251006142644_070785B.MP4
- Timestamp: 15234 ms
- Frame: 365
- Track: 17
- BBox: x=1014, y=46, w=266, h=659
- CLIP confidence: 85% (head covering)
- Version: v1

Full-Frame Format:

<date>__<video_stem>__t<ts_ms>__FRAME__v<ver>.webp

Example:
20251006__20251006142644_070785B__t15234__FRAME__v1.webp

INDEX.csv Schema

dataset,date,video_rel,video_stem,frame_idx,ts_ms,track_id,x1,y1,w,h,person_conf,covered_conf,file_type,crop_file,sha1,bboxes_json,annotated,pipeline_ver,yolo_batch,clip_batch,nvdec,created_utc

# Example rows:
Park_R,20251006,20251006\20251006142644_070785B.MP4,20251006142644_070785B,365,15234,17,1014,46,266,659,0.92,0.85,crop,20251006__20251006142644_070785B__t15234__f365__trk017__x1014y46w266h659__c85__v1.webp,a3f2c8b9...,,,v1,64,384,1,2025-10-06T14:30:22Z
Park_R,20251006,20251006\20251006142644_070785B.MP4,20251006142644_070785B,365,15234,,,,,,,frame,20251006__20251006142644_070785B__t15234__FRAME__v1.webp,d4e1a2c7...,"[{""x1"":1014,""y1"":46,""w"":266,""h"":659,""conf"":0.92,""track"":17}]",1,v1,64,384,1,2025-10-06T14:30:22Z

Column Definitions:

• dataset: Source camera (Park_R, Park_F, Movie_F, Movie_R)
• date: YYYYMMDD
• video_rel: Relative path from dataset root
• video_stem: Filename without .MP4 extension
• frame_idx: Frame number in video
• ts_ms: Timestamp in milliseconds
• track_id: ByteTrack ID (empty for FRAME rows)
• x1,y1,w,h: Bounding box (empty for FRAME rows)
• person_conf: YOLOv8 detection confidence
• covered_conf: CLIP head covering confidence (0-100 scale, empty if disabled)
• file_type: "crop" or "frame"
• crop_file: Relative filename
• sha1: File hash for integrity
• bboxes_json: All detections in frame (FRAME rows only)
• annotated: 1 if boxes drawn on frame, 0 otherwise
• pipeline_ver: Semantic version tag
• yolo_batch: YOLO batch size used
• clip_batch: CLIP batch size used (0 if disabled)
• nvdec: 1 if NVDEC used, 0 otherwise
• created_utc: ISO 8601 timestamp

Implementation Details

Processing Pipeline

[MP4 Videos]
     │
     ▼
[NVDEC Decoder (GPU)]
  RGB tensor → CUDA Stream A
     │
     ▼
[YOLOv8s Detection]
  Batched (64 frames)
  FP16, conf=0.35
     │
     ▼
[ByteTrack Tracking]
  IoU=0.5, max_age=10
     │
     ├──────────────────────► [Full-Frame Saver]
     │                        (optional, downscaled, annotated)
     ▼
[ROI Align (GPU)]
  Extract crops on GPU
     │
     ▼
[CLIP Classification]  ◄────── (optional)
  Batched (384 crops)
  FP16, threshold=0.80
     │
     ▼
[Deduplication Filter]
  SSIM ≥ 0.92
  Rate cap: 12/min/track
     │
     ▼
[Async I/O Thread Pool]
  WebP encode (q=85)
  Shard INDEX writes
     │
     ▼
[Final Merge]
  INDEX.csv

GPU Optimization Strategy

Dual CUDA Streams:

• Stream A: YOLOv8 detection
• Stream B: CLIP classification
• Overlap compute + memory transfers

Dynamic Batching:

• Accumulate frames until batch size reached
• Process immediately on timeout (100ms)
• Keep GPU pipeline full (80-90% utilization)

Memory Management:

• Pinned memory for faster CPU↔GPU transfers
• Pre-allocated tensor buffers
• Stream-ordered operations

Decoder Priority:

1. NVDEC (GPU hardware decoder) - 5-10x faster
2. CPU fallback (OpenCV) if NVDEC unavailable
3. Multi-threaded DataLoader (8-12 workers)

Performance Targets (RTX 4080 16GB)

Configuration Tuning

If GPU util < 70%:

1. Increase batch sizes: yolo_batch=80, clip_batch=448
2. Verify NVDEC active (check nvdec_active in response)
3. Increase parallel workers: max_workers=4

If CUDA OOM:

1. Reduce CLIP batch first: clip_batch=256
2. Then reduce YOLO batch: yolo_batch=48
3. Disable full-frame saves: save_full_frame=false

If disk I/O bottleneck:

1. Disable full-frame: save_full_frame=false
2. Reduce quality: full_frame_maxw=960, WebP q=75
3. Use faster storage (NVMe SSD)

CLI Equivalent

# Basic usage
python -m src.cli.run_multi_dates \
  --root "G:\My Drive\PROJECTS\INVESTIGATION\DASHCAM\Park_R" \
  --out parsed\ALL_CROPS \
  --dates 20251006 20251007 \
  --use-nvdec --conf 0.35 --iou 0.5

# Advanced usage with CLIP filtering
python -m src.cli.run_multi_dates \
  --root "G:\My Drive\PROJECTS\INVESTIGATION\DASHCAM\Park_R" \
  --out parsed\ALL_CROPS \
  --dates 20251006 20251007 20251008 \
  --use-nvdec \
  --yolo-batch 64 \
  --clip-batch 384 \
  --clip-threshold 0.80 \
  --conf 0.35 \
  --iou 0.5 \
  --save-full-frame \
  --draw-boxes \
  --parallel 3

Example Interactions

Example 1: Basic Detection

User: "Extract all humans from Park_R videos on October 6"

Skill invokes:

{
  "mode": "extract_humans",
  "roots": ["G:\\My Drive\\PROJECTS\\INVESTIGATION\\DASHCAM\\Park_R\\20251006"],
  "confidence": 0.35,
  "single_output_dir": "parsed\\ALL_CROPS",
  "nvdec": true
}

Example 2: Advanced with CLIP

User: "Scan Park_R for October 6-8, filter for people with head coverings at 80% confidence, save annotated frames, max GPU usage"

Skill invokes:

{
  "mode": "extract_humans",
  "roots": [
    "G:\\My Drive\\PROJECTS\\INVESTIGATION\\DASHCAM\\Park_R\\20251006",
    "G:\\My Drive\\PROJECTS\\INVESTIGATION\\DASHCAM\\Park_R\\20251007",
    "G:\\My Drive\\PROJECTS\\INVESTIGATION\\DASHCAM\\Park_R\\20251008"
  ],
  "confidence": 0.35,
  "iou": 0.50,
  "yolo_batch": 64,
  "clip_filter": {
    "enabled": true,
    "threshold": 0.80,
    "batch": 384
  },
  "nvdec": true,
  "save_full_frame": true,
  "draw_boxes": true,
  "single_output_dir": "parsed\\ALL_CROPS",
  "parallel": {
    "max_workers": 3
  }
}

Example 3: Low-Resource Mode

User: "Process Park_R October 6 with minimal GPU memory"

Skill invokes:

{
  "mode": "extract_humans",
  "roots": ["G:\\My Drive\\PROJECTS\\INVESTIGATION\\DASHCAM\\Park_R\\20251006"],
  "confidence": 0.35,
  "yolo_batch": 32,
  "clip_filter": {
    "enabled": false
  },
  "nvdec": false,
  "save_full_frame": false,
  "single_output_dir": "parsed\\ALL_CROPS"
}

Safety & Guardrails

Do Not

• ❌ Move or delete source MP4 files
• ❌ Infer gender unless explicitly enabled (sensitive, noisy)
• ❌ Process videos without user consent
• ❌ Share outputs containing identifiable persons

Do

• ✅ Verify GPU availability before processing
• ✅ Enforce longitude sign corrections for GPS overlays
• ✅ Maintain audit trail in INDEX.csv
• ✅ Log versions, batches, NVDEC usage
• ✅ Handle OOM gracefully with suggestions

Resume Safety

• Idempotent: skip already-processed crops by filename
• Shard-based: partial runs can resume
• Index integrity: SHA1 hashes verify file correctness

Testing & Verification

Pre-Run Checks

# GPU availability
assert torch.cuda.is_available(), "CUDA required"
assert torch.cuda.device_count() > 0, "No GPU found"

# Model files
assert Path("models/yolov8s.pt").exists(), "YOLOv8 model missing"

# Output directory writable
output_dir = Path("parsed/ALL_CROPS")
output_dir.mkdir(parents=True, exist_ok=True)
assert os.access(output_dir, os.W_OK), "Output dir not writable"

Post-Run Verification

# Check outputs exist
assert Path("parsed/ALL_CROPS/INDEX.csv").exists()
assert len(list(Path("parsed/ALL_CROPS").glob("*.webp"))) > 0

# Validate INDEX.csv
df = pd.read_csv("parsed/ALL_CROPS/INDEX.csv")
assert df['crop_file'].notna().all()
assert df['person_conf'].between(0, 1).all()

# Sample roundtrip
sample = df.sample(1).iloc[0]
assert Path(f"parsed/ALL_CROPS/{sample['crop_file']}").exists()

# GPU utilization check
assert gpu_util_avg > 0.70, f"Low GPU util: {gpu_util_avg}"

Dependencies

Required

• Python 3.10+
• PyTorch 2.0+ with CUDA 11.8+
• ultralytics (YOLOv8)
• transformers (CLIP)
• opencv-python
• pillow
• pandas
• numpy

Optional (Performance)

• NVIDIA Video Codec SDK (NVDEC)
• TensorRT (future optimization)
• nvJPEG (GPU JPEG encoding)

Installation

cd "G:\My Drive\PROJECTS\APPS\Human_Detection"
pip install -r requirements.txt

Troubleshooting

Common Issues

1. CUDA Out of Memory

Error: CUDA out of memory. Tried to allocate 2.50 GiB
Solution: Reduce batch sizes
  yolo_batch: 64 → 48 → 32
  clip_batch: 384 → 256 → 128

2. NVDEC Not Available

Warning: NVDEC unavailable, falling back to CPU decode
Solution: Check NVIDIA driver version (≥525.60)
  GPU must support Video Codec SDK
  Verify with: nvidia-smi --query-gpu=name --format=csv

3. Low GPU Utilization

Warning: GPU util only 45%
Solutions:
  1. Increase batch sizes (if VRAM allows)
  2. Enable NVDEC: nvdec=true
  3. Increase parallel workers: max_workers=4
  4. Check CPU bottleneck (use more DataLoader workers)

4. Slow Processing

Performance: 0.8 videos/min (expected 3-4)
Diagnostics:
  1. Check disk I/O (use SSD)
  2. Verify NVDEC active (5-10x faster than CPU)
  3. Profile with: python -m torch.utils.bottleneck script.py

Future Enhancements

Planned

• TensorRT optimization (2-4x CLIP speedup)
• Multi-GPU sharding (process different dates on different GPUs)
• GPU JPEG/WebP encoding (nvJPEG)
• Real-time streaming mode

Under Consideration

• Face recognition integration
• Gender classification (opt-in only, with warnings)
• Action recognition (walking, standing, etc.)
• Multi-camera fusion (correlate detections across cameras)

Version History

v1.0 (Current)

• Initial release
• YOLOv8s + ByteTrack + CLIP
• NVDEC support
• Unified output directory
• Global INDEX.csv

References

• YOLOv8 Documentation
• ByteTrack Paper
• CLIP Paper
• NVIDIA Video Codec SDK

Contact & Support

For issues or questions:

1. Check parsed/ALL_CROPS/run_*.log for error details
2. Review GPU diagnostics: nvidia-smi
3. Validate input paths exist and are readable
4. Verify CUDA/PyTorch installation: python -c "import torch; print(torch.cuda.is_available())"