Reliable animal behavior analysis depends not only on the behavioral test itself but also on how video is captured, detected, organized, and reviewed. A suitable tracking setup can turn recorded movement into structured trajectory, activity, and event data.
Improving the setup therefore involves more than choosing software; it means aligning video acquisition, observation areas, detection settings, and analysis methods with the study design.
Plan the Video Acquisition Around the Experiment
Video collection should fit the timing and sequence of the behavioral task. Repeated trials can become harder to organize when recording starts and stops are handled separately for each animal. A structured acquisition workflow helps keep the video dataset aligned with the experimental schedule.
SANS-AI Video Tracking Software includes start-time, duration, recording-count, and interval-time controls. The interval setting can accommodate activities such as changing animals or cleaning the maze between recordings. Multiple camera acquisition experiments can also be supported at the same time.
Define Observation Areas Before Tracking
A tracking system needs clear spatial definitions to convert movement into meaningful measurements. In a maze or open-field test, researchers may need to distinguish target zones, surrounding areas, or several regions within the same experiment.
The software provides drawing tools for observation and analysis areas and can load conventional maze paradigms. The same area configuration can also be applied to multiple videos with one click. This can simplify setup when the experimental geometry remains consistent across repeated recordings.
Match Detection Settings to the Scene
Detection quality affects the trajectory that appears in the final dataset. Background conditions, animal appearance, and scene characteristics can vary between behavioral tests, so a single detection method may not suit every recording.
SANS-AI offers several detection approaches, including automatic selection, grayscale-gradient analysis, static- and dynamic-background processing, and difference detection. Additional controls such as erosion, dilation, and area screening can be used to refine the detected animal contour when scene conditions require adjustment.
Capture More Than the Body Path
For some experiments, whole-body movement does not contain enough detail to describe the behavior being studied. More precise positional information can be useful when the research question involves orientation, interaction, or fine movement patterns.
The software uses deep learning–based keypoint detection to identify the animal’s full contour, nose, body center, and tail base—yielding rich trajectory and behavioral data from multiple anatomical points, not just a single centroid. It supports mice, rats, guinea pigs, zebrafish, Drosophila, and other small animals.
Build Analysis Around Specific Behavioral Events
Animal behavior analysis becomes more useful when movement data are connected to the events defined by the study. A trajectory can show where an animal moved, while event rules can identify when it entered an area, remained there for a specified duration, or met several experimental conditions.
The experiment-control module includes time events, area detection, multi-area settings, duration rules, and condition-based requirements. Researchers can use these functions to organize the behavioral sequence before statistical analysis rather than relying on a general movement trace alone.
Review and Refine the Recorded Trajectory
Even when automated detection is used, recorded trajectories may contain fragments or irregular points that require review. A better workflow includes a post-processing stage instead of treating every detected path as ready for analysis.
SANS-AI provides two trajectory-processing options. Smoothing averages data between multiple points to reduce irregular edges, while Max Move Distance excludes trajectory fragments.
It exceeds a defined movement distance and can help address false-detection interference. Researchers can then review the resulting path before continuing with analysis.
Combine Automated Tracking With Visual Outputs
A strong video workflow should make behavioral results easier to inspect as well as quantify. SANS-AI can display movement trajectories and generate heatmaps that represent factors such as spatial distribution, elapsed time, velocity, and acceleration.
The analysis module also allows researchers to select technical indicators for statistical summaries.
Manual annotation remains optional for behaviors requiring direct observation. Researchers can log additional events via 26 keyboard shortcuts, merging human input with automated motion tracking. Real-time tracking and offline multi-threaded batch processing accommodate both live experiments and large-scale video analysis.
Build the Setup Around the Research Question
Better animal behavior analysis comes from matching each part of the tracking workflow to the experimental question. Video acquisition should fit the session structure, spatial settings should reflect the test design, and detection methods should suit the recording environment.
Trajectory processing and visualization then provide additional ways to review the resulting data.
For labs using automated behavioral video analysis, SANS-AI supports rodents and other small animals across domains including learning & memory, anxiety & depression, social interaction, circadian rhythms, home-cage behavior, and drug dependence.
It integrates seamlessly into workflows involving maze-specific setups, repeated recordings, trajectory analysis, and quantitative behavioral assessment.
BioMed Easy provides the SANS-AI Video Tracking Software for laboratories developing or refining animal behavior analysis workflows. Researchers can contact BioMed Easy to discuss the species, behavioral paradigm, recording setup, and analysis requirements for a new or existing tracking workflow.
