Dynamically parallel CAMSHIFT: GPU accelerated object tracking in digital video

<p>"The CAMSHIFT algorithm is widely used for tracking dynamically sized and positioned objects in real-time applications. In spite of its extensive study on the platform of sequential CPU, its research on massively parallel Graphical Processing Unit (GPU) platform is quite limited. In this work, we designed and implemented two different parallel algorithms for CAMSHIFT using CUDA. The first design performs calculations on the GPU, but requires iterative data transfers back to the host CPU for condition checking, which bottlenecks the entire program. In the second design, we propose an enhanced parallel reduction-based CAMSHIFT using dynamic parallelism to reduce overhead of data transfers between the CPU and GPU. Test results for a 400 by 400 search window show that the second design is up to five times faster than the first design and nine times faster than a pure CPU implementation. We also investigate the deployment of dynamic parallelism for multiple object tracking using CAMSHIFT"--Leaf iv.</p>

Name thesis:degree_name

Master of Science (MS) in Computer Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year

2016

Author dc:creator

• Perry, Matthew J.

• Digital video--Mathematical models
• Graphics processing units
• Parallel algorithms
• Coding theory
• CUDA (Computer architecture)
• Graphics and Human Computer Interfaces
• Theory and Algorithms

Statement dc:rights

• Access is available to all users

Repository record dc:identifier

https://dc.ewu.edu/theses/382

OAI identifier oai:identifier

oai:dc.ewu.edu:theses-1382