Supercharging Object Detection in Video: from Glacial to Lightning Speed

http://www.youtube.com/watch?v=rR6EIakYSZ0 In the following series I will explore different tools and techniques for doing object detection in streaming video in real time or faster. Starting with the baseline Python detector running slowly and gradually picking up speed. In these series In the course of these posts we will explore optimizing object detection in videos. We … Continue reading Supercharging Object Detection in Video: from Glacial to Lightning Speed →

Supercharging Object Detection in Video: Optimizing Decoding and Graph Feeding

In the previous post we validated our install and ran a simple detector in C++. It is now time to start optimizing it. Source code for the finished project is here. Optimizing Video Decoding If we build and run the video_reader.cpp OpenCV sample, we will observe a staggering performance improvement available in OpenCV for decoding … Continue reading Supercharging Object Detection in Video: Optimizing Decoding and Graph Feeding →

Zooming Through Euler Path: Supercharging with GPU

So, continuing where we left off: Walking the Euler Path: Intro Visualizing Graphs Walking the Euler Path: GPU for the Road Walking the Euler Path: PIN Cracking and DNA Sequencing For the Win And finally I ran the GPU-enabled algorithm for finding the Euler path. And the results: Generating euler graph: vertices = 10,485,760; avg … Continue reading Zooming Through Euler Path: Supercharging with GPU →

Walking the Euler Path: GPU for the Road

Continuation of the previous posts: Intro Visualization GPU Digression I was going to talk about something else this week but figured I'd take advantage of the free-hand format and digress a bit. Continuing the travel metaphor and remembering Julius Cesar's "alea iacta", we'll talk about GPU algorithms, for which I invariably use my favorite Aela.CUDA … Continue reading Walking the Euler Path: GPU for the Road →

Computing Self-Organizing Maps in a Massively Parallel Way with CUDA. Part 1: F#

By 2017, it is expected that GPUs will no longer be an external accelerator to a CPU; instead, CPUs and GPUs will be integrated on the same die with a unified memory architecture. Such a system eliminates some of accelerator architectures’ historical challenges, including requiring the programmer to manage multiple memory spaces, suffering from bandwidth … Continue reading Computing Self-Organizing Maps in a Massively Parallel Way with CUDA. Part 1: F# →