Video Surveillance System Incorporating Expert Decision-making Process: A Case Study on Detecting Calving Signs in Cattle
Ryosuke Hyodo, Susumu Saito, Teppei Nakano, Makoto Akabane, Ryoichi, Kasuga, Tetsuji Ogawa

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFood Supply Chain Traceability
