Video-Based Tracking and Incremental Learning Applied to Rodent Behavioral Activity Under Near-Infrared Illumination

摘要

This paper describes a noninvasive video tracking system for measurement of rodent behavioral activity under near-infrared (NIR) illumination, where the rodent is of a similar color to the background. This novel method allows position tracking in the dark, when rodents are generally most active, or under visible light. It also improves current video tracking methods under low-contrast conditions. We also manually extracted rodent features and classified three common behaviors (sitting, walking, and rearing) using an inductive algorithm—a decision tree (ID3). In addition, we proposed the use of a time–spatial incremental decision tree (ID5R), with which new behavior instances can be used to update the existing decision tree in an online manner. These were implemented using incremental tree induction. Open-field locomotor activity was investigated under “visible” ( $hbox{460.5} {-} hbox{561.1} hbox{nm}$), 880- and 940-nm wavelengths of NIR, as well as a “dark” condition consisting of a very small level of NIR illumination. A widely used NIR crossbeam-based tracking system (Activity Monitor, MED Associates, Inc., Georgia, VT) was used to record simultaneous position data for validation of the video tracking system. The classification accuracy for the set of new test data was 81.3%.