1. ECE 562 Computer Architecture and Design Project: Improving Feature Extraction Using SIFT on GPU Rodrigo Savage, Wo-Tak Wu

2. Overview Application: Object tracking in real time Challenges: 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

3. Scale Invariant Feature Transform (SIFT) Input: image Output: keypoints

4. 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

5. 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%)

6. 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

7. Backup Slides

8. References SiftGPU available at http://cs.unc.edu/~ccwu/siftgpu/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

9. Test image with keypoints

12. Algorithm