Fare evasion is a problem for public transport companies, with LSTM models
this issue can help companies get an analytical insight into where this issue
occurs the most, to prevent capital loss. In addition to the financial burden
this problem causes, having more inspectors is not enough to alleviate the
problem. The purpose of this study is to find a different way to predict fare
evasion in the public transport sector. Through the use of keypoint extractions
of passengers in video footage, an LSTM model is trained on those keypoints to
help predict the actions of passengers between payments and evasions. The
results were promising when it came to predicting the actions of passengers on
real-time footage. Thus a sophisticated approach can help to decrease the fare
evasion problem. A ReID model can be used alongside the LSTM model for better
accuracy, as there is always the chance that a person might only pay for the
fare at a later stage. With both models, it is possible for public transport
companies to start narrowing down where the root of their fare evasion problems
emerges.

使用 LSTM 模型和关键点提取，对公共交通领域的乘客进行行为预测，以减少逃票问题。同时，结合 ReID 模型提高准确性，帮助公共交通公司定位逃票问题的根源。

深度神经网络方法用于逃票

A Deep Neural Network Approach to Fare Evasion

We present the top ranked solution for the AISG-SLA Visual Localisation
Challenge benchmark (IJCAI 2023), where the task is to estimate relative motion
between images taken in sequence by a camera mounted on a car driving through
an urban scene.
For matching images we use our recent deep learning based matcher RoMa.
Matching image pairs sequentially and estimating relative motion from point
correspondences sampled by RoMa already gives very competitive results -- third
rank on the challenge benchmark.
To improve the estimations we extract keypoints in the images, match them
using RoMa, and perform structure from motion reconstruction using COLMAP. We
choose our recent DeDoDe keypoints for their high repeatability. Further, we
address time jumps in the image sequence by matching specific non-consecutive
image pairs based on image retrieval with DINOv2. These improvements yield a
solution beating all competitors.
We further present a loose upper bound on the accuracy obtainable by the
image retrieval approach by also matching hand-picked non-consecutive pairs.

我们提出了一个在 AISG-SLA 视觉定位挑战竞赛中获得第一名的解决方案，该任务是估计通过一个车载相机在城市场景中连续拍摄的图像之间的相对运动。我们使用我们最近基于深度学习的匹配器 RoMa 对图像进行匹配，并从中样本点进行相对运动估计，取得了非常有竞争力的成绩 - 在挑战竞赛中排名第三。为了提高估计准确度，我们提取图像中的关键点，使用 RoMa 进行匹配，并使用 COLMAP 进行结构运动重建。我们选择最近的 DeDoDe 关键点因其高可重复性。此外，为了解决图像序列中的时间跳跃问题，我们使用 DINOv2 进行图像检索，并匹配特定的非连续图像对。这些改进使我们的解决方案超过了所有竞争对手。我们还通过匹配手动选择的非连续图像对，给出了图像检索方法可达到的精度的上限。

利用前沿的基于深度学习的图像匹配技术重建稀疏图像中的大场景

Leveraging Cutting Edge Deep Learning Based Image Matching for  Reconstructing a Large Scene from Sparse Images

Human-object interaction (HOI) detection for capturing relationships between
humans and objects is an important task in the semantic understanding of
images. When processing human and object keypoints extracted from an image
using a graph convolutional network (GCN) to detect HOI, it is crucial to
extract appropriate object keypoints regardless of the object type and to
design a GCN that accurately captures the spatial relationships between
keypoints. This paper presents the human and object keypoint-based extension
module (HOKEM) as an easy-to-use extension module to improve the accuracy of
the conventional detection models. The proposed object keypoint extraction
method is simple yet accurately represents the shapes of various objects.
Moreover, the proposed human-object adaptive GCN (HO-AGCN), which introduces
adaptive graph optimization and attention mechanism, accurately captures the
spatial relationships between keypoints. Experiments using the HOI dataset,
V-COCO, showed that HOKEM boosted the accuracy of an appearance-based model by
a large margin.

本文提出了一种基于人体和物体关键点的扩展模块（HOKEM）和一个自适应的图最优化和注意机制的人体 - 物体自适应图卷积网络（HO-AGCN），用于提高语义理解图像中人和物体之间的交互检测精度。实验结果表明，HOKEM 显著提高了外观模型的准确性。