Applied Computer Vision System for Primate Welfare


Profs. Yixin Zhu and Yizhou Wang’s groups are developing computer vision algorithms to detect and identify primates (monkeys and apes) and their behaviors. They are working with primate video data from their collaborators of Professor Federico Rossano’s Comparative Cognition Laboratory (CCL) at the University of California, San Diego (UCSD). One of their projects uses video footage of motion-triggered trail cameras that are installed in the enclosures of several different groups of macaque monkeys that are housed at a large animal sanctuary in the United States. Due to large enclosure sizes at this primate sanctuary, the animal caretakers spend significant amounts of their time on monitoring the well-being of their animals. The goal of this project is to build a system for the caretakers that uses the above-mentioned computer vision algorithms to detect and identify specific macaques from trail camera footage. This will help the animal sanctuary to provide better care for their animals and can further aid behavioral researchers at CCL. The challenge and could be scaled to other sanctuaries in the future.


  • Implement the Computer Vision algorithms for detecting and identifying monkeys into a user-friendly system that can aid animal caretakers at the sanctuary with monitoring their animals.
  • Travel to the US to work both in San Diego (CCL) and Texas (Monkey Sanctuary) where you will setup this system.


Your Skills

The ideal candidates for this project have experience with computer vision, UX design for building user-friendly programs (with a GUI) and should be interested in learning about primate behavior and welfare.

Project Stages

  • Initial planning and software development with the research team at PKU and San Diego (remotely)
  • Traveling to San Diego (USA) to test the system and work with research group at CCL
  • Setup and monitor system at the primate sanctuary in Texas

The system should allow staff at the sanctuary to identify monkeys on video footage in a user-friendly way. It further should consist of a database that enables caretakers to search when a monkey was last seen on video footage. If possible, the algorithms could also run locally on a device (e.g., Raspberry Pi) with a camera that could be placed inside the enclosures directly.