Optical flow is a classical task that is important to the vision community.
Classical optical flow estimation uses two frames as input, whilst some recent
methods consider multiple frames to explicitly model long-range information.
The former ones limit their ability to fully leverage temporal coherence along
the video sequence; and the latter ones incur heavy computational overhead,
typically not possible for real-time flow estimation. Some multi-frame-based
approaches even necessitate unseen future frames for current estimation,
compromising real-time applicability in safety-critical scenarios. To this end,
we present MemFlow, a real-time method for optical flow estimation and
prediction with memory. Our method enables memory read-out and update modules
for aggregating historical motion information in real-time. Furthermore, we
integrate resolution-adaptive re-scaling to accommodate diverse video
resolutions. Besides, our approach seamlessly extends to the future prediction
of optical flow based on past observations. Leveraging effective historical
motion aggregation, our method outperforms VideoFlow with fewer parameters and
faster inference speed on Sintel and KITTI-15 datasets in terms of
generalization performance. At the time of submission, MemFlow also leads in
performance on the 1080p Spring dataset. Codes and models will be available at:
this https URL

MemFlow 是一种实时的光流估计和预测方法，通过使用内存中的历史运动信息实现光流的估计和预测，并通过适应不同视频分辨率来提高性能。

MemFlow: 光流估计与记忆预测

MemFlow: Optical Flow Estimation and Prediction with Memory

Automatic License Plate Recognition (ALPR) is becoming a popular study area
and is applied in many fields such as transportation or smart city. However,
there are still several limitations when applying many current methods to
practical problems due to the variation in real-world situations such as light
changes, unclear License Plate (LP) characters, and image quality. Almost
recent ALPR algorithms process on a single frame, which reduces accuracy in
case of worse image quality. This paper presents methods to improve license
plate recognition accuracy by tracking the license plate in multiple frames.
First, the Adaptive License Plate Rotation algorithm is applied to correctly
align the detected license plate. Second, we propose a method called Character
Time-series Matching to recognize license plate characters from many
consequence frames. The proposed method archives high performance in the
UFPR-ALPR dataset which is \boldmath$96.7\%$ accuracy in real-time on RTX A5000
GPU card. We also deploy the algorithm for the Vietnamese ALPR system. The
accuracy for license plate detection and character recognition are 0.881 and
0.979 $mAP^{test}$@.5 respectively. The source code is available at
this https URL

提出在多帧中跟踪车牌并通过自适应车牌旋转算法和字符时间序列匹配方法提高车牌识别精确度的研究。在 UFPR-ALPR 数据集上达到 96.7% 的实时准确率，用于越南 ALPR 系统的车牌检测和字符识别准确率分别达到 0.881 和 0.979 mAP@0.5。