How to Implement Unsupervised Video Segmentation and Tracking

Video segmentation and tracking are essential tasks in computer vision that have gained traction in recent years, especially with the advancement of unsupervised learning techniques. In this guide, we’ll walk you through setting up the code for unsupervised bottom-up video motion segmentation, known as uNLC, a reimplementation of the original NLC algorithm. We’ll also cover video tracking and provide you with user-friendly steps to get started.

Understanding the Code: An Analogy

Think of video segmentation as a chef preparing a multi-course meal. Each ingredient (or video frame) must be carefully selected and prepared (segmented) before it can be cooked (tracked) into a delicious dish (output video). Just as a chef uses different techniques like chopping, boiling, or frying to bring diverse elements together, the segmentation process requires the integration of optical flow, motion saliency, and other low-level descriptors to create a cohesive representation of the video.

Setup Instructions

To set everything up and get started with video segmentation and tracking, follow the steps outlined below:

• Install Optical Flow

  cd videoseglib
  git clone https://github.com/pathak22/pyflow.git
  cd pyflow
  python setup.py build_ext -i
  python demo.py  # -viz option to visualize output

• Install Dense CRF Code

  cd videoseglib
  git clone https://github.com/lucasb-eyer/pydensecrf.git
  cd pydensecrf
  python setup.py build_ext -i
  PYTHONPATH=.:$PYTHONPATH python examples/inference.py examples/im1.png examples/anno1.png examples/out_new1.png
  # Compare out_new1.png and out1.png -- they should be the same

• Install Appearance Saliency

  cd videoseglib
  git clone https://github.com/ruanxiang/mr_saliency.git

• Install Kernel Temporal Segmentation Code

  # cd videoseglib
  # wget http://pascal.inrialpes.fr/data2/potapov/med_summaries/kts_ver1.1.tar.gz
  # tar -zxvf kts_ver1.1.tar.gz
  mv kts_ver1.1 kts
  # rm -f kts_ver1.1.tar.gz
  # Edit kts/cpd_nonlin.py to remove weave dependency. This change is only necessary if you don't have weave installed.

• Convert Them to Modules

  cd videoseglib
  cp __init__.py mr_saliency
  cp __init__.py kts

• Run Temporal Segmentation

  time python vid2shots.py -imdir /home/pathak/local/data/trash/my_nlc/imseq/v21 -out /home/pathak/local/data/trash/my_nlc/nlc_out

• Run NLC Segmentation

  cd videoseg/src
  time python nlc.py -imdir /home/pathak/local/data/trash/my_nlc/imseq3_tmp -out /home/pathak/local/data/trash/my_nlc/nlc_out -maxsp 400 -iters 100

• Run Tracker

  cd videoseg/src
  time python dm_tracker.py -fgap 2 -seed 2905 -vizTr -dmThresh 0 -shotFrac 0.2 -matchNbr 20 -postTrackHomTh -1 -preTrackHomTh 10

• Run CRF Sample

  cd videoseg/src
  time python crf.py -inIm ../lib/pydensecrf/examples/im1.png -inL ../lib/pydensecrf/examples/anno1.png -out ../lib/pydensecrf/examples/out_new2.png

• Run Full Pipeline

  cd videoseg/src
  time python run_full.py -out /home/pathak/local/data/AllSegInit -in /home/pathak/fbcode/experimental/deep_learning/pathak/videoseg/dataset/Samples.txt

Troubleshooting Tips

While setting everything up, you may encounter a few issues. Here are some common troubleshooting ideas:

• Ensure that all dependencies and environment variables are correctly set. Like a recipe requires all ingredients, your code needs the right configurations.
• If the optical flow components are not functioning, double-check that the correct versions have been installed from GitHub.
• For visualization problems, verify that you have the necessary libraries for rendering outputs; this is equivalent to having a sharp knife in the kitchen—without it, cutting becomes messy!

If you find this work useful in your research, consider visiting the original papers and repositories for more information:
Unsupervised Learning using Unlabeled Videos and
CVPR 2017 Paper Repository.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.