Data-Driven Analysis of Animal Behavioral Patterns

被引:0
作者
Menaker, Tom [1 ]
机构
[1] Univ Haifa, Haifa, Israel
来源
SEVENTH INTERNATIONAL CONFERENCE ON ANIMAL-COMPUTER INTERACTION, ACI 2020: Embodied Dialogues | 2020年
关键词
animal data mining; computational ethology; VIDEO TRACKING; MOTION; SYSTEM; DEPTH; DOGS;
D O I
10.1145/3446002.3446004
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Measurement of behavior is a major challenge in many animal-related disciplines, including ACI. This usually requires choosing specific parameters for measuring, related to the investigated hypothesis. Therefore, a key challenge is determining what a priori parameters are informational for a given experiment. The scope of this challenge is raised even further by the emerging computational approaches for animal detection and tracking, as the automation of behavioral measurement makes the possibilities for measuring behavioral parameters practically endless. This research approaches these challenges by proposing a framework for guiding the researchers decision making in their future data analysis. The envisioned framework is data-driven in the sense that it applies data mining techniques for obtaining insights from experimental data for guiding the choice of certain behavioral parameters. Here, we present the envisioned Data-Driven Framework for Behavioral Pattern Analysis framework and its components and discuss it's current topics and challenges.
引用
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页数:6
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