Video analytics systems based on deep learning models are often opaque and
brittle and require explanation systems to help users debug. Current model
explanation system are very good at giving literal explanations of behavior in
terms of pixel contributions but cannot integrate information about the
physical or systems processes that might influence a prediction. This paper
introduces the idea that a simple form of causal reasoning, called a regression
discontinuity design, can be used to associate changes in multiple key
performance indicators to physical real world phenomena to give users a more
actionable set of video analytics explanations. We overview the system
architecture and describe a vision of the impact that such a system might have.

基于深度学习模型的视频分析系统常常是不透明且易碎的，需要解释系统来帮助用户进行调试。现有的模型解释系统善于以像素贡献的方式给出行为的字面解释，但无法整合可能影响预测的物理或系统过程的信息。本文引入了回归不连续设计这种简单形式的因果推理，用于将多个关键绩效指标的变化与物理世界现象相关联，以给用户提供更可行的视频分析解释集合。我们概述了系统架构，并描述了这种系统可能带来的影响的愿景。

探索视频分析系统中的因果物理错误发现

Towards Causal Physical Error Discovery in Video Analytics Systems

Practical video analytics systems that are deployed in bandwidth constrained
environments like autonomous vehicles perform computer vision tasks such as
face detection and recognition. In an end-to-end face analytics system, inputs
are first compressed using popular video codecs like HEVC and then passed onto
modules that perform face detection, alignment, and recognition sequentially.
Typically, the modules of these systems are evaluated independently using
task-specific imbalanced datasets that can misconstrue performance estimates.
In this paper, we perform a thorough end-to-end evaluation of a face analytics
system using a driving-specific dataset, which enables meaningful
interpretations. We demonstrate how independent task evaluations, dataset
imbalances, and inconsistent annotations can lead to incorrect system
performance estimates. We propose strategies to create balanced evaluation
subsets of our dataset and to make its annotations consistent across multiple
analytics tasks and scenarios. We then evaluate the end-to-end system
performance sequentially to account for task interdependencies. Our experiments
show that our approach provides consistent, accurate, and interpretable
estimates of the system's performance which is critical for real-world
applications.

基于一个驾驶特定数据集进行端到端评估，提出了创造平衡评估子集和使其注释一致的策略，以及顺序评估端到端系统性能以解决任务间的相互依赖性问题。实验证明我们的方法能够提供一致、准确和可解释的系统性能估计，对实际应用至关重要。