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The Smart Vivarium

The Smart Vivarium. Serge Belongie, Kristin Branson, Keith Jenn é , Vincent Rabaud, Phil Richter, Geert Schmid-Schoenbein, John Wesson http://vision.ucsd.edu. Abstract.

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The Smart Vivarium

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  1. The Smart Vivarium Serge Belongie, Kristin Branson, Keith Jenné, Vincent Rabaud, Phil Richter, Geert Schmid-Schoenbein, John Wesson http://vision.ucsd.edu

  2. Abstract UCSD's vivariums contain thousands of cages of mice, making close monitoring of individual mice impossible. Both animal care and experimental data collection would be improved by constant monitoring of mice. The goal of the Smart Vivarium Project is to automatically analyze the behavior of mice in a cage from video surveillance. The first step in automated behavior analysis of individual mice involves estimating the position of each mouse in each frame of video. Next, the mouse positions and domain-specific cues are used to label the instantaneous behaviors of each mouse. Finally, the individual mouse behaviors over time are catalogued and analyzed to determine the health of each mouse.

  3. Problem • UCSD and other research centers’ vivariums contain many thousands of mice. • Because of the huge number of mice, even the simplest monitoring tasks are time-consuming and expensive. • Close monitoring of the mice is prohibitively expensive.

  4. Figure 1: A room full of racks of cages of mice.

  5. Our Solution • We are creating a system to automatically monitor mouse behavior from video surveillance. • The system uses computer vision and artificial intelligence techniques to track individual mice and identify their behaviors. • We therefore call our system the Smart Vivarium.

  6. System Requirements • Our system must be nonintrusive; it must not affect the well-being or behavior of the mice. • Our system must be general: it must identify many different types of behaviors. • For practical reasons, our system must be easy to add to existing vivariums. • As mice are nocturnal, our system must function with little visible light. • To our knowledge, ours is the only system that meets these requirements.

  7. System Goals • Our system will: • Perform basic management tasks. • Analyze animal well-being and health. • Log reproduction statistics.

  8. Management • Management tasks include: • Census measurement. • Supply monitoring. • Waste monitoring. • Identification of individual mice. • Environment monitoring.

  9. Census Measurement • Census measurement tasks include: • Identifying individual mouse properties, e.g. breed and age. • Counting the number of mice per cage. • Counting the number of cages per room. Figure 2: Example images of three mice in a cage.

  10. Supply Monitoring • The state of supplies must be monitored: • Food, Water, Bedding, Enrichment, Caging materials

  11. Waste Monitoring • Metabolic products • Ammonia • Heat - humidity • Feces

  12. Identification • The identities of each mouse must be tracked.

  13. Environment Monitoring • Room • Air • Light • Humidity • Light intensity • Location in room • Equipment • Percent of time cage open

  14. Basic Well Being • When evaluating the health of a mouse, the following factors are pertinent: • Body composition. • Activities (normal and abnormal) performed. • Amount of food and water eaten and drunk. • Amount of urination and defecation.

  15. Body composition • Body composition parameters are: • Body mass • Lean body mass • Body fat • Total body water • Percent body water • Change in total body water per unit time

  16. Normal Activity • Monitoring normal activity levels requires measuring: • Total number of steps • Total distance traveled • Amount of time in motion • Amount of time climbing • Average velocity • Grooming • Sleeping/Resting • Exploratory novel stimuli

  17. Abnormal Activity • Abnormal behaviors include: • Circling • Rolling • Stumbling • Scratching • Death

  18. Drinking and Eating • Time spent at water lick • Volume of water intake • Frequency of water intake • Time spent eating • Volume of food intake • Frequency of food intake • Food preference

  19. Urination and Defecation • Volume of urine production • Urine specific gravity • Frequency of micturation • Volume of feces production • Frequency of elimination • Fecal water content

  20. Reproduction • The following reproduction activities must be monitored: • Breeding • Nesting • Parturition • Lactation/nursing

  21. Breeding and Nesting • Number of matings • Mating time per mating • Total mating time • Completed breeding • Time producing a nest

  22. Parturition and Lactation • Time in delivery • Total animals per litter • Total animals lost per litter • Total nursing time • Nursing frequency

  23. System Description • Our system uses video taken from the side of the cage. • For night surveillance, we plan on using a near-IR camera. • We first estimate the position of each mouse in each frame of video. • Next, the mouse positions and domain-specific cues are used to label the instantaneous behaviors of each mouse. • Finally, the individual mouse behaviors over time are catalogued and analyzed to determine the health of each mouse.

  24. Identity Tracking Mouse Positions Behavior Analysis Video sequence Behavior Identification Health Reports Behavior Labels

  25. Tracking Identities • Tracking mice in a cage is a uniquely challenging tracking task because • The mice are virtually identical. • The mice have few, if any, trackable features. • The motion of the mice is erratic. • We thus created a new tracking algorithm to track mouse identities, described in: “Three Brown Mice: See How They Run” to appear in VS-PETS 2003. • Preliminary tracking results are below:

  26. (a) Frames 49, 64, 80, 104. (b) Frames 516, 525, 539, 556. Figure: Example frames showing the raw image frames from an occlusion event in the top row and the Gaussian parameters estimated by our algorithm. The ellipses correspond to 2 standard deviations of the Gaussians.

  27. (a) (t,x) raw image data (360 × 776 pixels): a single scanline of the image at every frame. (b) (t,x) predicted image (360 × 776 pixels): membership of points in a scanline of the image at every frame. Figure: Tracking results (t,x) plot of results. The x-axis in these images is time and the y-axis is the x-axis of the original frame. Each column corresponds to the same scanline of a different frame.

  28. Behavior Analysis • The next step is to label the behavior of each mouse in each frame. • Our current system labels the following behaviors: • Sitting • Standing • Walking • Cleaning • It also keeps track of the number of steps taken by each individual mouse. • Please view the video demos for more tracking and behavior analysis results.

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