Currently, there are computer vision systems that help us with tasks that
would be dull for humans, such as surveillance and vehicle tracking. An
important part of this analysis is to identify traffic anomalies. An anomaly
tells us that something unusual has happened, in this case on the highway. This
paper aims to model vehicle tracking using computer vision to detect traffic
anomalies on a highway. We develop the steps of detection, tracking, and
analysis of traffic: the detection of vehicles from video of urban traffic, the
tracking of vehicles using a bipartite graph and the Convex Hull algorithm to
delimit moving areas. Finally for anomaly detection we use two data structures
to detect the beginning and end of the anomaly. The first is the QuadTree that
groups vehicles that are stopped for a long time on the road and the second
that approaches vehicles that are occluded. Experimental results show that our
method is acceptable on the Track4 test set, with an F1 score of 85.7% and a
mean squared error of 25.432.

本篇论文通过计算机视觉建立车辆跟踪模型，用于检测高速公路上的交通异常，并提出了检测、跟踪和分析交通的步骤，具体包括从城市交通视频中检测车辆、使用双分图和凸包算法进行车辆跟踪，以及使用两种数据结构检测异常的起点和终点。实验结果显示该方法在 Track4 测试集上表现可接受，F1 分数为 85.7%，均方误差为 25.432。

MEDAVET: 基于车辆交通的空间和时间结构的交通车辆异常检测机制

MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial  and temporal structures in vehicle traffic

In modern fulfillment warehouses, agents traverse the map to complete endless
tasks that arrive on the fly, which is formulated as a lifelong Multi-Agent
Path Finding (lifelong MAPF) problem. The goal of tackling this challenging
problem is to find the path for each agent in a finite runtime while maximizing
the throughput. However, existing methods encounter exponential growth of
runtime and undesirable phenomena of deadlocks and rerouting as the map size or
agent density grows. To address these challenges in lifelong MAPF, we explore
the idea of highways mainly studied for one-shot MAPF (i.e., finding paths at
once beforehand), which reduces the complexity of the problem by encouraging
agents to move in the same direction. We utilize two methods to incorporate the
highway idea into the lifelong MAPF framework and discuss the properties that
minimize the existing problems of deadlocks and rerouting. The experimental
results demonstrate that the runtime is considerably reduced and the decay of
throughput is gradually insignificant as the map size enlarges under the
settings of the highway. Furthermore, when the density of agents increases, the
phenomena of deadlocks and rerouting are significantly reduced by leveraging
the highway.

本文主要研究如何在 lifelong MAPF 中使用 highway 方法解决地图规模增大、密度增大等问题，以减少 deadlocks 现象并优化 throughput，实验证明 runtime 得到改进。