ECE 562 Computer Architecture and Design

Project: Improving Feature Extraction Using SIFT on GPU

Rodrigo Savage, Wo-Tak Wu

OverviewApplication:

• Object tracking in real timeChallenges:

• Static Scene• Moving objects• Occluding• Collision• Disappearing• Rotation• Scaling

Divide and Conquer:• Feature Extraction and Tracking

Focus on:• Feature Extraction, used SIFT • Improve an existing implementation with GPU

Scale Invariant Feature Transform (SIFT)

Input: imageOutput: keypoints

GPU Implementation• Selected the GPU implementation by Sinha et al. at UNC

at Chapel Hill• Open-source SiftGPU available (latest V4.00, Sept. 2012)• SIFT well suited to be implemented on GPU

• Tens of thousands of threads handle subsets of data without communication with each other

Attempts to Speed Up

• Tackled the 2 most time consuming processing steps• Blurring images with Gaussian low-pass filter

• Changed pixel data access pattern• Used different schemes of data partitioning

• Keypoint descriptor (128-element vector) calculations• Optimize code in the kernel

• Used usual optimization techniques• Changed GPU memory usage

• Threads management• Experimented with kernel parameters• Maximized usage of available threads

Result: Reduced descriptor compute time from 73 to 22 ms (70%)

Conclusion

• Existing implementation is already pretty good• Hard to take full advantage of the architecture. Need to

have good understanding of• Memory architecture• Thread usage

• CUDA C/C++ compiler (nvcc) optimizes code in different ways. Need to experiment to gain performance

• Hard to debug code running on GPU• Visual Profiler can provide valuable insights on code

behaviors

Backup Slides

References

• SiftGPU available at http://cs.unc.edu/~ccwu/siftgpu/• D. G. Lowe, “Distinctive image features from scale-invariant keypoints,”

International Journal of Computer Vision, November 2004.• Sudipta N. Sinha et al., “GPU-based Video Feature Tracking And Matching,”

Technical Report TR 06-012, Department of Computer Science, UNC Chapel Hill, May 2006.

• NVIDIA GeForce GT 640M LE • CUDA Cores: 384 • Total available graphics memory: 4095 MB

Test image with keypoints

Algorithm

Algorithm

Algorithm