Killer Bunny Rabbit Detector
An Application of Object Detection
James Maher
Image from: http://thereelcult.com/wp‐content/uploads/2012/04/Monty‐Python‐rabbit_400.jpg
Outline
•
•
•
•
•
Introduction
Previous Work
Methodology
Results
Discussion and Conclusion
Introduction
• My Rabbit Problem
• PyCon 2012 Talk
– Militarizing Your Backyard with Python…, by: Kurt Grandis [1].
• Project Goal
– Apply a more sophisticated Computer Vision algorithm to detect objects
Images from [1]. Previous Work
• Background Segmentation
– Issues
• Ghosts [2]
• Multi‐step process is computationally intensive
• Feature Extraction
– SIFT [3]
– Histogram of Oriented Gradients (HOGs) [4]
Images from [2,5]. Image from [3]. Methodology
• Followed P. Felzenszwalb, et al.’s, Discriminatively Trained Deformable Parts Model [6],[7].
• Deformable parts are parts that can change shape and position, but only within a limited range.
Image from [8]. An early attempt at object detection through deformable parts. Methodology
• Histograms of Oriented Gradients (HOGs) are used for feature extraction
• The magnitude and direction of the gradients are calculated for each pixel and combined into one histogram per cell.
– A cell is defined as 8x8 pixels
Gradient Direction
Gradient Magnitude
Gradient Masks
1 0
1
1 0
1
Methodology
• Each pixel’s gradient direction and orientation votes into a histogram for the cell
– 9 orientation bins and 4 magnitude bins were used
• These histograms are normalized by blocks of 2x2 cells – Each cell is normalized by it’s 8 neighbors
Image from [6]. Methodology
• A feature pyramid of HOGs was used to match the root filter and the deformable parts
Image from [7]. • For implementation, deformable parts used twice the resolution of the root filter
Methodology
• Two factors determined the score for potential matches:
1. How well the image’s HOG features matched the trained model 2. How much the parts were deformed from the trained model
• This results in:
max
∙ Φ x, z
∈
∙
,
∙
,
∙
,
Methodology
• A visual representation of the filters and penalty terms for a trained model:
Image from [6]. Methodology
• A Latent Support Vector Machine (LSVM) is max ∙ Φ x, z
used to train the term in:
∈
• The latent variables are in the z term
Methodology
• OpenCV contains a library for LatentSVMs
– Must provide a trained model. The only trained models available are for objects in the PASCAL competition.
• MATLAB source code is available at: http://people.cs.uchicago.edu/~rbg/latent/
• Code only compiles on Mac and Linux
• Modifications for training a new model are non‐
trivial, but details are included in my paper
Methodology
• Used 126 training images, from videos of rabbits near my house
• Wrote MATLAB scripts to extract training images and create files training the model
• Held back 14 testing photos from rabbits near Golden and 10 photos from the original movies that were not used in training the model. Results
Results
• None of the testing images from Golden were detected
• Detection/Non‐Detection in the training videos was dependent on how close the camera was to the rabbit
– 50% of the video clips were detected
• No false positives. Bounding box surrounded the rabbit each time
• Increasing the number/variety of training examples increases detection
Discussion and Conclusion
• Good
– Images similar to training images were detected
– No false positives
• Bad
– Algorithm is not fast enough to be used with real‐
time, 30fps video
– Must be close to the target
• Notes:
– Closer images worked better than distant images
References
[1] K. Grandis, Militarizing Your Backyard with Python: Computer Vision and the Squirrel Hordes. PyCon USA 2012.
[2] R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, “Detecting moving objects, ghosts, and shadows in video streams,” Pattern Anal. Mach. Intell. IEEE Trans., vol. 25, no. 10, pp. 1337–1342, 2003.
[3] D. G. Lowe, “Distinctive image features from scale‐invariant keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, 2004.
[4] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” presented at the International Conference on Computer Vision & Pattern Recognition, 2005, vol. 1, pp. 886–893.
[5] W. Hoff, “Motion‐Based Segmentation,” presented at the EGGN 512: Computer Vision, 2013.
[6] P. Felzenszwalb, D. McAllester, and D. Ramanan, “A Discriminatively Trained, Multiscale, Deformable Part Model,” in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, 2008, pp. 1–8.
[7] P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan, “Object Detection with Discriminatively Trained Part Based Models,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, vol. 32.
References
[8] M. A. Fischler and R. A. Elschlager, “The representation and matching of pictorial structures,” Comput. Ieee Trans., vol. 100, no. 1, pp. 67–92, 1973.