Related Experiment Video
Updated: Aug 9, 2026
Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device (ALDM) Test Systems
Published on: May 5, 2015
A Smart-Alarm Detection and Deterrent System for Long-Tailed Macaque Intrusion in Human Residential Zones
Kanyanut Homsapaya1, Janya Jadejaroen2, Orawan Watchanupaporn1
1Department of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Chonburi, Thailand.
Background:
Human-macaque conflict has become an increasing concern in urban and residential areas. Existing deterrent systems, particularly ultrasonic-based approaches relying on fixed frequencies or simple motion detection, have limited effectiveness and high false alarm rates. Moreover, most systems lack integration between detection, risk assessment, and adaptive response mechanisms.
Objective:
To develop a smart-alarm system for detecting and mitigating long-tailed macaque intrusion in human residential environments.
Methods:
Computer vision-based object detection was integrated with real-time risk assessment using distance and group size as key parameters. The system dynamically activates deterrent sound signals and sends real-time alerts via communication platforms. The system was deployed and evaluated in a real-world setting (Sriracha Campus, Kasetsart University).
Results:
The system could effectively detect macaques and provide appropriate, timely deterrence responses, thereby reducing human-macaque conflict.
Conclusions:
This novel AI-based integrated framework combines detection, behavior analysis, and risk assessment for nonlethal wildlife management in shared human-animal environments.
