facebookresearch/co-tracker
Co Tracker
Co-tracker is a PyTorch point tracking framework and dense point tracking model designed to map the motion of individual pixels throughout a video. It functions as a video pixel tracker that predicts point trajectories and visibility masks across sequences of video frames.
The project includes a computer vision training pipeline that utilizes teacher-student knowledge distillation. This allows for the generation of pseudo-labels from unannotated real video data to fine-tune pre-trained models and reduce the gap between synthetic and real data environments.
The framework provides capabilities for video motion analysis and visual tracking evaluation, including tools for rendering trajectories and visibility masks to inspect tracking accuracy. It supports both offline context and online streaming processing for video sequence analysis.
Features
- Dense Pixel Tracking - Provides dense pixel tracking capabilities to map the motion of individual pixels throughout a video.
- Differentiable Visibility Masks - Predicts binary visibility scores for each tracked point to identify when pixels leave the frame or are occluded.
- Point Tracking - Tracks individual or dense sets of pixels across video frames while handling occlusions and object exits.
- Dense Tracking Architectures - Implements a neural network architecture that maps the motion of individual pixels throughout a video.
- Teacher-Student Distillation - Uses teacher-student knowledge distillation to train models on unannotated real video data via pseudo-labels.
- Point Tracking Frameworks - Provides a PyTorch-based framework for tracking dense sets of pixels across video frames using deep learning.
- Model-Assisted Labelers - Generates labels from teacher models using unannotated real videos to bridge the gap between synthetic and real data.
- Visual Point Trackers - Provides a system for predicting point trajectories and visibility masks across sequences of video frames.
- Pseudo-Label Video Generation - Generates ground truth pseudo-labels for unannotated videos using teacher models to train more robust trackers.
- Pixel Motion Analysis - Analyzes point trajectories and visibility over time to understand pixel movement within video sequences.
- Iterative Refinement Workflows - Utilizes iterative refinement loops to update point locations and correct drift over long video sequences.
- Pixel-Level Attention - Implements pixel-level cross-attention mechanisms to compute correspondences between feature vectors across video frames.
- Video Sequence Architectures - Processes video frames in both forward and backward directions to maintain tracking continuity during occlusions.
- Computer Vision Training - Provides a training pipeline for fine-tuning point tracking models using teacher-student distillation.
- Model Fine-Tuning - Allows fine-tuning of pre-trained tracking models on real video datasets to improve performance in specific environments.
- Pseudo-Label Fine-Tuning - Updates pre-trained models using real video datasets and automatically generated labels to improve performance.
- Model Performance Evaluators - Measures the precision of predicted pixel trajectories against ground truth data to validate performance.
- Tracking Visualization - Renders predicted point trajectories and visibility masks over original video frames to inspect accuracy.
- Visual Tracking Evaluation - Provides utilities to measure tracking performance by comparing predicted pixel trajectories against ground truth data.
- Video Input Processing - Supports both offline context and online streaming processing for video sequence analysis.
- Frame Memory Buffers - Maintains a sliding-window buffer of previous frames to optimize memory and computational overhead during online inference.
Star history
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