Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent
years, due to its ability to detect object motion out of sight. Most previous
works on NLOS tracking rely on active illumination, e.g., laser, and suffer
from high cost and elaborate experimental conditions. Besides, these techniques
are still far from practical application due to oversimplified settings. In
contrast, we propose a purely passive method to track a person walking in an
invisible room by only observing a relay wall, which is more in line with real
application scenarios, e.g., security. To excavate imperceptible changes in
videos of the relay wall, we introduce difference frames as an essential
carrier of temporal-local motion messages. In addition, we propose PAC-Net,
which consists of alternating propagation and calibration, making it capable of
leveraging both dynamic and static messages on a frame-level granularity. To
evaluate the proposed method, we build and publish the first dynamic passive
NLOS tracking dataset, NLOS-Track, which fills the vacuum of realistic NLOS
datasets. NLOS-Track contains thousands of NLOS video clips and corresponding
trajectories. Both real-shot and synthetic data are included. Our codes and
dataset are available at this https URL

本研究提出了一种纯被动式的非直射式跟踪方法，仅通过观察一个继电器墙来追踪走在隐形房间中的人，利用差分帧作为时空局部运动信息的必要携带者，提出了 PAC-Net，填补了现有非直射式数据集的空缺。

实时被动非直视跟踪：传播与校准

Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking

Inductive transfer learning aims to learn from a small amount of training
data for the target task by utilizing a pre-trained model from the source task.
Most strategies that involve large-scale deep learning models adopt
initialization with the pre-trained model and fine-tuning for the target task.
However, when using over-parameterized models, we can often prune the model
without sacrificing the accuracy of the source task. This motivates us to adopt
model pruning for transfer learning with deep learning models. In this paper,
we propose PAC-Net, a simple yet effective approach for transfer learning based
on pruning. PAC-Net consists of three steps: Prune, Allocate, and Calibrate
(PAC). The main idea behind these steps is to identify essential weights for
the source task, fine-tune on the source task by updating the essential
weights, and then calibrate on the target task by updating the remaining
redundant weights. Under the various and extensive set of inductive transfer
learning experiments, we show that our method achieves state-of-the-art
performance by a large margin.

本文提出一种基于模型剪枝的迁移学习的简单且有效的方法 PAC-Net，包括三个步骤：修剪、分配和校准。该方法在各种各样和广泛的归纳迁移学习实验中取得了遥遥领先的表现。