SiamBOMB: A Real-time AI-based System for Home-cage Animal Tracking, Segmentation and Behavioral Analysis

Xi Chen; Hao Zhai; Danqian Liu; Weifu Li; Chaoyue Ding; Qiwei Xie; Hua Han

IJCAI 2020poster0 citations

SiamBOMB: A Real-time AI-based System for Home-cage Animal Tracking, Segmentation and Behavioral Analysis

Xi Chen, Hao Zhai, Danqian Liu, Weifu Li, Chaoyue Ding, Qiwei Xie, Hua Han

Abstract

Biologists often need to handle numerous video-based home-cage animal behavior analysis tasks that require massive workloads. Therefore, we develop an AI-based multi-species tracking and segmentation system, SiamBOMB, for real-time and automatic home-cage animal behavioral analysis. In this system, a background-enhanced Siamese-based network with replaceable modular design ensures the flexibility and generalizability of the system, and a user-friendly interface makes it convenient to use for biologists. This real-time AI system will effectively reduce the burden on biologists.

Computer Vision: generalMachine Learning: general

BibTeX

@inproceedings{ijcai2020p776,
  title     = {SiamBOMB: A Real-time AI-based System for Home-cage Animal Tracking, Segmentation and Behavioral Analysis},
  author    = {Chen, Xi and Zhai, Hao and Liu, Danqian and Li, Weifu and Ding, Chaoyue and Xie, Qiwei and Han, Hua},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Christian Bessiere},
  pages     = {5300--5302},
  year      = {2020},
  month     = {7},
  note      = {Demos},
  doi       = {10.24963/ijcai.2020/776},
  url       = {https://doi.org/10.24963/ijcai.2020/776},
}