Project Information

  • Category: Computer Vision Pipeline
  • Type: Semi-Supervised Learning
  • Application: Multi-Object Tracking & Detection
  • Publication: arXiv:2406.17183
  • GitHub: View Code
43.1% mAP50 Improvement
on AnimalTrack
24.5% Recall Improvement
on GMOT-40

Project Overview

POPCat is an innovative computer vision pipeline that revolutionizes video annotation for multi-object tracking, crowd-counting, and industrial video tasks. By combining state-of-the-art particle tracking, segmentation, and object detection algorithms, it creates a semi-supervised system that significantly reduces annotation time while maintaining human-level accuracy.

Key Innovation

Exploits multi-target and temporal features of video data through particle tracking to expand human-provided target points across multiple frames, generating large volumes of semi-supervised annotations.

Impact

Achieved significant improvements on challenging benchmarks: 24.5% recall improvement on GMOT-40, 43.1% mAP50 improvement on AnimalTrack, and 9.4% mAP50 improvement on Visdrone-2019.

🔬 Technical Pipeline

1

Strategic Frame Selection & Manual Annotation

Custom Tkinter GUI enables users to select key frames from video sequences and manually annotate target points on multiple objects. The number of frames and points can be adjusted based on video length and annotation requirements, providing flexibility for different use cases.

Tkinter GUI OpenCV Python
⬇
2

Particle Tracking Propagation

Leverages advanced particle tracking algorithms (PIPs & TAPIR) to propagate manually annotated points across subsequent video frames. The temporal propagation exploits multi-target features to maintain tracking accuracy across challenging scenarios including occlusions and camera movements.

PIPs Algorithm TAPIR (JAX) Particle Tracking
⬇
3

Segment Anything Model Integration

Meta's Segment Anything Model (SAM) uses the propagated particle points as prompts to generate precise object segmentations across the target frames. This approach enables accurate object boundary detection for densely populated video sequences with minimal manual intervention.

Segment Anything (SAM) Meta AI Point Prompting
⬇
4

Automated Bounding Box Generation

Converts segmentation masks into precise bounding boxes, creating a comprehensive training dataset for object detection. This automated process ensures consistent annotation quality across all generated samples.

Computer Vision Geometric Processing Dataset Generation
⬇
5

Weakly Supervised Training

Trains a YOLOv8 object detector on the automatically generated dataset of 1500 samples. This weakly supervised approach achieves high performance without requiring extensive manual annotation effort.

YOLOv8 PyTorch Weakly Supervised Learning
⬇
6

Full Video Inference

Deploys the trained model to annotate remaining video frames automatically, achieving 10 FPS processing speed. The system scales efficiently to handle complete video sequences with minimal computational overhead.

Real-time Inference Docker Deployment Production Ready

✨ Key Features & Capabilities

High-Speed Processing

Achieves 10 FPS annotation speed, representing a 10x improvement over traditional methods. Optimized algorithms and efficient implementation ensure real-time performance.

Minimal Manual Input

Requires only 30 manual point annotations to generate 1500+ training samples. Intelligent propagation algorithms maximize the value of minimal human input.

State-of-the-Art Algorithms

Integrates latest computer vision models including PIPs, TAPIR, SAM, and YOLOv8. Benchmarked against industry standards for optimal performance.

Production Ready

Fully containerized with Docker for seamless deployment. Used in production at ATS Automations with proven reliability and scalability.

Comprehensive Benchmarking

Thoroughly tested on GMOT-40 and Animal Track datasets. Performance metrics demonstrate significant improvements over existing annotation pipelines.

Multi-Algorithm Integration

Seamlessly combines PyTorch, JAX, OpenCV, and specialized computer vision libraries. Demonstrates advanced software engineering and system integration skills.

Technology Stack

Core Frameworks
Python PyTorch JAX OpenCV
Computer Vision Models
YOLOv8 Segment Anything (SAM) PIPs TAPIR
Development & Deployment
Docker Tkinter Git

Benchmark Datasets

GMOT-40 Dataset

Multi-object tracking benchmark featuring challenging scenarios with similar-looking targets, occlusions, and complex interactions in crowded environments.

AnimalTrack Dataset

Specialized dataset for animal behavior analysis featuring diverse species in natural environments with varying lighting conditions and camera movements.

Visdrone-2019 Dataset

Aerial video dataset captured by drone platforms, containing various objects with different scales, orientations, and densities in diverse scenarios.

Benchmark Performance

Comprehensive Evaluation on Challenging Datasets

POPCat was rigorously evaluated on three challenging multi-object tracking and detection benchmarks: GMOT-40, AnimalTrack, and Visdrone-2019. These datasets contain multiple similar-looking targets, camera movements, and other challenging features commonly seen in real-world scenarios.

Key Performance Improvements
  • ✓ GMOT-40: 24.5% recall improvement, 9.6% mAP50 improvement, 4.8% mAP improvement
  • ✓ AnimalTrack: 43.1% mAP50 improvement, 27.8% mAP improvement
  • ✓ Visdrone-2019: 7.5% recall improvement, 9.4% mAP50 improvement, 7.5% mAP improvement
  • ✓ Maintains human-level annotation accuracy while generating large-scale training data
3 Benchmark Datasets
Evaluated
✓ Published Research
arXiv:2406.17183

Interested in Learning More?

POPCat represents the cutting edge of computer vision pipeline development. I'm always excited to discuss technical details, potential applications, or collaboration opportunities.

Get In Touch View Code