# Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning

arXiv: https://arxiv.org/abs/1911.11170 · HTML: https://mlsanity.com/paper/1911.11170/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Updatable Siamese Tracker with Two-stage One-shot Learning](https://mlsanity.com/paper/2104.15049/) (2021)
- [Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking](https://mlsanity.com/paper/2205.11179/) (2022)
- [Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking](https://mlsanity.com/paper/2112.14016/) (2021)
- [Efficient Multi-Object Tracking on Edge Devices via Reconstruction-Based Channel Pruning](https://mlsanity.com/paper/2410.08769/) (2024)

## Abstract (arXiv)

We propose a novel meta-learning framework for real-time object tracking with efficient model adaptation and channel pruning. Given an object tracker, our framework learns to fine-tune its model parameters in only a few iterations of gradient-descent during tracking while pruning its network channels using the target ground-truth at the first frame. Such a learning problem is formulated as a meta-learning task, where a meta-tracker is trained by updating its meta-parameters for initial weights, learning rates, and pruning masks through carefully designed tracking simulations. The integrated meta-tracker greatly improves tracking performance by accelerating the convergence of online learning and reducing the cost of feature computation. Experimental evaluation on the standard datasets demonstrates its outstanding accuracy and speed compared to the state-of-the-art methods.