Video anomaly detection research is generally evaluated on short, isolated
benchmark videos only a few minutes long. However, in real-world environments,
security cameras observe the same scene for months or years at a time, and the
notion of anomalous behavior critically depends on context, such as the time of
day, day of week, or schedule of events. Here, we propose a context-aware video
anomaly detection algorithm, Trinity, specifically targeted to these scenarios.
Trinity is especially well-suited to crowded scenes in which individuals cannot
be easily tracked, and anomalies are due to speed, direction, or absence of
group motion. Trinity is a contrastive learning framework that aims to learn
alignments between context, appearance, and motion, and uses alignment quality
to classify videos as normal or anomalous. We evaluate our algorithm on both
conventional benchmarks and a public webcam-based dataset we collected that
spans more than three months of activity.

该研究论文提出了一种面向长期监控场景的上下文感知视频异常检测算法 Trinity，该算法适用于拥挤场景，其中个体难以跟踪，异常是由速度、方向或群体运动缺失引起的。通过对上下文、外观和动作之间的对齐质量进行学习，Trinity 使用对齐质量来将视频分类为正常或异常，该算法在传统基准和我们收集的覆盖超过三个月的公共网络摄像头数据集上进行了评估。

上下文感知的长期数据集视频异常检测

Context-aware Video Anomaly Detection in Long-Term Datasets

Interest modeling in recommender system has been a constant topic for
improving user experience, and typical interest modeling tasks (e.g.
multi-interest, long-tail interest and long-term interest) have been
investigated in many existing works. However, most of them only consider one
interest in isolation, while neglecting their interrelationships. In this
paper, we argue that these tasks suffer from a common "interest amnesia"
problem, and a solution exists to mitigate it simultaneously. We figure that
long-term cues can be the cornerstone since they reveal multi-interest and
clarify long-tail interest. Inspired by the observation, we propose a novel and
unified framework in the retrieval stage, "Trinity", to solve interest amnesia
problem and improve multiple interest modeling tasks. We construct a real-time
clustering system that enables us to project items into enumerable clusters,
and calculate statistical interest histograms over these clusters. Based on
these histograms, Trinity recognizes underdelivered themes and remains stable
when facing emerging hot topics. Trinity is more appropriate for large-scale
industry scenarios because of its modest computational overheads. Its derived
retrievers have been deployed on the recommender system of Douyin,
significantly improving user experience and retention. We believe that such
practical experience can be well generalized to other scenarios.

我们提出了一种名为 “Trinity” 的新型和统一的框架，在检索阶段解决了兴趣遗忘问题和改进了多兴趣建模任务。在 Douyin 的推荐系统中部署后，显著改善了用户体验和留存，我们相信这种实际经验可以推广到其他场景。