Video Surveillance System Incorporating Expert Decision-making Process: A Case Study on Detecting Calving Signs in Cattle

TL;DR

This paper presents an interpretable video surveillance AI system that incorporates expert decision-making processes to improve livestock monitoring, specifically for detecting calving signs in cattle, verified through a user study.

Contribution

The study introduces a framework for making video surveillance AI systems interpretable by integrating domain expert reasoning, demonstrated through a calving detection system evaluated with livestock farmers.

Findings

Participants preferred the interpretable system over black-box AI.

Most users referred to the reasoning explanations in decision-making.

Majority of users expressed willingness to adopt the proposed system.

Abstract

Through a user study in the field of livestock farming, we verify the effectiveness of an XAI framework for video surveillance systems. The systems can be made interpretable by incorporating experts' decision-making processes. AI systems are becoming increasingly common in real-world applications, especially in fields related to human decision-making, and its interpretability is necessary. However, there are still relatively few standard methods for assessing and addressing the interpretability of machine learning-based systems in real-world applications. In this study, we examine the framework of a video surveillance AI system that presents the reasoning behind predictions by incorporating experts' decision-making processes with rich domain knowledge of the notification target. While general black-box AI systems can only present final probability values, the proposed framework can present information relevant to experts' decisions, which is expected to be more helpful for their decision-making. In our case study, we designed a system for detecting signs of calving in cattle based on the proposed framework and evaluated the system through a user study (N=6) with people involved in livestock farming. A comparison with the black-box AI system revealed that many participants referred to the presented reasons for the prediction results, and five out of six participants selected the proposed system as the system they would like to use in the future. It became clear that we need to design a user interface that considers the reasons for the prediction results.