Top 6 Alternatives to DeepLabCut: AI Tracking Without the Complexity (2025 Edition)

Conduct Science | Behavioral Tracking Without the Coding Headaches

Why Researchers Need Simpler AI Tracking

DeepLabCut revolutionized behavior research by introducing open-source, deep learning-based pose estimation. It proved that markerless, body-point detection was possible at scientific precision.

However, for many labs, DeepLabCut is powerful—but not easy. Annotating hundreds of frames is time-consuming. Training neural networks requires advanced computing setups. Even simple tasks demand Python coding and troubleshooting.

That’s why many researchers are now seeking alternatives to DeepLabCut for behavioral tracking—solutions that eliminate complexity while maintaining scientific accuracy.

#1. ConductVision (by Conduct Science)

The Gold Standard for No-Code, Markerless Behavioral Tracking

Why it’s #1:

• No annotation required — AI automatically detects 11 key body points (nose, tail, ears, limbs, spine)
  
• Markerless multi-animal tracking (4+ animals in one arena)
  
• 30+ frames per second real-time analysis
  
• Works for mice, rats, zebrafish, Drosophila, and even birds
  
• Supports a variety of behavioral paradigms: Social Interaction, Open Field, Light/Dark Box, T-Maze, Novel Object Recognition, and more
  
• Batch video processing for rapid data throughput
  
• Outputs behavioral metrics, CSV data, heatmaps, and trajectory plots
  
• Infrared and visible light compatibility
  
• Plug-and-play with no coding, scripting, or manual segmentation
  

Best for: Labs needing high-speed, multi-species, markerless tracking with zero technical barriers. ConductVision stands out among all alternatives to DeepLabCut for behavioral tracking due to its no-code, plug-and-play approach.

#2. EthoVision XT (by Noldus)

Established, but Modular and Expensive. For example, even basic pose tracking requires annotation and code.

Strengths:

• Zone-based and path tracking for rodents and fish
  
• Optional modules for social interaction, anxiety, cognition
  

Limitations:

• Contour-based tracking (not full pose estimation)
  
• Markerless multi-animal tracking limited and requires add-ons
  
• Expensive to build a complete setup
  
• Requires calibration and software learning curve
  

Best for: Labs with funding and technical support seeking robust traditional tracking.

#3. Motrack (by MotionMouse)

Lightweight AI Tracking for Simple Arenas

Strengths:

• AI-based tracking without markers
  
• Good for single-animal open field or maze tasks
  

Limitations:

• Single-animal tracking only
  
• No body-point data; tracks only center of mass
  
• Limited behavioral assay templates
  

Best for: Small labs running basic locomotion studies.

#4. ezTrack

Open-Source Simplicity for Basic Needs

Strengths:

• Free and relatively simple to install
  
• Zone-based analysis for open field and place preference
  

Limitations:

• No body-point detection (only whole-body tracking)
  
• No support for aquatic species
  
• Limited to single animal per video
  
• Requires basic coding in Jupyter Notebooks
  

Best for: Labs on a budget running simple exploratory tasks.

#5. VAME (Variational Animal Motion Embeddings)

Advanced but Complex Deep Learning

Strengths:

• Deep unsupervised learning to discover behavioral motifs
  
• High analytical resolution
  

Limitations:

• Requires Python scripting and GPU computing
  
• Steep learning curve
  
• Best used for highly specialized studies
  

Best for: Computational behavioral neuroscience labs with machine learning expertise.

As a result, researchers seek simpler, AI-powered alternatives.

#6. SLEAP (Social LEAP Estimates Animal Poses)

Great for Multi-Animal, But Requires Technical Skills

Strengths:

• Markerless pose tracking for multiple animals
  
• Tracks interactions with fine detail
  

Limitations:

• Manual frame labeling required to train networks
  
• High GPU and technical requirements
  
• Complex installation and pipeline management
  

Best for: Labs needing detailed social interaction mapping, willing to invest in training and hardware.

Why ConductVision Is the Best Alternative to DeepLabCut for Behavioral Tracking

DeepLabCut opened the door to precision in behavior tracking. However, ConductVision takes that promise further—delivering powerful AI analysis with none of the coding, annotation, or GPU complexity.

Whether you’re analyzing social behavior in mice, locomotion in zebrafish, or exploratory patterns in flies, ConductVision makes multi-animal, multi-species tracking accessible to every lab.

It’s time to track smarter, not harder.
👉 Explore ConductVision Today.

References:

1. Mathis, A. et al. (2018). DeepLabCut: Markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience.
   
2. Noldus Information Technology. EthoVision XT. https://www.noldus.com/ethovision-xt
   
3. OpenBehavior Project. ezTrack. https://openbehavior.com/eztrack/
   
4. Pereira, T. D. et al. (2022). SLEAP: A deep learning system for multi-animal pose tracking. Nature Methods.
   
5. Luxem, K. et al. (2022). VAME: Variational Animal Motion Embeddings for unsupervised behavioral analysis. Nature Communications.