Top 5 Alternatives to ANY-maze for Behavioral Tracking (2025 Edition)

Behavioral Research Needs Are Evolving

ANY-maze has been a staple of behavioral tracking for years—helping labs automate simple video tracking for rodents across various mazes. However, as scientific demands evolve, researchers increasingly seek higher frame rates, markerless tracking, multi-animal tracking, and AI-driven precision to uncover deeper behavioral insights.

Today’s behavioral neuroscience labs need more than just zone entry data—they need real-time posture analysis, multi-species support, and batch processing power.

In this article, we’ll review the top alternatives to ANY-maze in 2025, with a focus on scalability, speed, flexibility, and next-gen technology.

#1. ConductVision (by Conduct Science)

The Most Advanced and User-Friendly Behavioral Tracking Solution

Why it’s #1: ConductVision redefines what’s possible in automated behavioral tracking:

• 30+ frames per second high-speed tracking for real-time precision
  
• Markerless tracking with automatic detection of 11 anatomical body points
  
• Supports mice, rats, zebrafish, Drosophila, and birds—without any tags
  
• Seamless tracking for social behavior, maze performance, locomotor assays, and multi-species studies
  
• Batch video processing for rapid, high-throughput analysis
  
• Intuitive user interface—no coding or complex setup required
  
• Fully compatible with Conduct Science mazes (Barnes Maze, Elevated Plus Maze, Light/Dark Box, Open Field, and more)
  
• Infrared and visible light camera support
  

Best for: Labs ready to upgrade to AI-powered, scalable tracking without sacrificing ease of use.

#2. ANY-maze (by Stoelting Co.)

Reliable but Becoming Outpaced by New Technologies

Strengths:

• Easy setup and zone-based analysis
  
• Good for single-animal, traditional maze tasks
  
• Widely adopted in academic labs
  

Limitations:

• Low frame rate (~6–8 fps standard)
  
• Contour-based tracking (no body point detection)
  
• Limited markerless multi-animal tracking
  
• Basic locomotor outputs—lacks advanced posture/interaction data
  

Difficult to scale for high-throughput or multi-species studies

#3. EthoVision XT (by Noldus)

Powerful, but High Complexity and Cost

Strengths:

• Widely used for maze and aquatic tracking
  
• Good modularity (add-ons for social behavior, cognition, etc.)
  

Limitations:

• Expensive to fully outfit for multi-paradigm studies
  
• Requires complex calibration for multi-animal or small organisms
  
• Less intuitive for new users
  

Best for: Larger, well-funded labs needing extensive modular control.

#4. DeepLabCut

Powerful Open-Source Pose Estimation with High Barriers to Entry

Strengths:

• Deep learning-based body point tracking
  
• Highly customizable for any species
  

Limitations:

• Requires manual annotation of training sets
  
• Coding and computational knowledge needed
  
• Batch processing requires scripting
  

Best for: Computational neuroscience labs with machine learning expertise.

#5. ToxTrac

A Free, Lightweight Tool for Simple Locomotion Analysis

Strengths:

• Open-source and free to use
  
• Simple locomotion and zone tracking
  

Limitations:

• Contour tracking only (no body point data)
  
• No posture, social interaction, or aquatic model tracking
  
• Limited support for complex paradigms or batch analysis
  

Best for: Labs on a tight budget or students starting basic behavior projects.

Final Thoughts: ConductVision—A True Step Forward

If you’re seeking the next generation of behavioral tracking, ConductVision delivers a rare combination of precision, versatility, and simplicity. With high-speed tracking, true markerless AI detection, and support for multiple species, it surpasses legacy systems like ANY-maze while remaining easy enough for any lab to adopt.

Ready to transform your behavioral research?
Learn more about ConductVision today

References:

1. Stoelting Co. ANY-maze. https://www.anymaze.co.uk/
   
2. Noldus Information Technology. EthoVision XT. https://www.noldus.com/ethovision-xt
   
3. Mathis, A. et al. (2018). DeepLabCut. Nature Neuroscience.
   

Rodriguez, A. et al. (2018). ToxTrac. Scientific Reports.

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