Supercharging Object Detection in Video: First App

Tensorflow C++ Video Detector It is time to validate all this arduous setup work, run our first C++ detector and reap the first benefits. You may clone this repository, which is a fork of this repository, modified and adapted to the modern times. Ensuring the Right Build Paths Note the following excerpt from CMakeLists.txt: The … Continue reading Supercharging Object Detection in Video: First App →

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 →

Supercharging Object Detection in Video: TensorRT 5

Source code for the finished project is here. NVIDIA TensorRT is a framework used to optimize deep networks for inference by performing surgery on graphs trained with popular deep learning frameworks: Tensorflow, Caffe, etc. Preparing the Tensorflow Graph Our code is based on the Uff SSD sample installed with TensorRT 5.0. The guide together with … Continue reading Supercharging Object Detection in Video: TensorRT 5 →

HoloLens Object Detection

We will explore running object detection on-device with HoloLens, using Unity game engine as our development platform. AR Academy is a great introduction to all aspects of HoloLens development. It has 300-level tutorials that demonstrate how to connect the device to Azure Cognitive Services to perform machine learning tasks. There are no samples of performing … Continue reading HoloLens Object Detection →

On the Margins: Non-maximum Suppression with Tensorflow

I'm writing a series of posts on supercharging object detection inference performance in video streams using Tensorflow and cool tech from NVIDIA: step-by-step, starting from 6 fps all the way up to 230. But before I start, this small post is about a cool little gem, which I think is often overlooked. Anyone in the … Continue reading On the Margins: Non-maximum Suppression with Tensorflow →

Scripting Video Editing with F# and FFmpeg

Computer vision should not be confused with image processing (as we all know). I love building computer vision pipelines, but sometimes menial tasks of pure image processing, automated editing come up. Suppose you had the same astronauts from one of the previous posts participating in a study, where they are actually filmed watching something, say … Continue reading Scripting Video Editing with F# and FFmpeg →

Detecting Faces with Dlib from F#. IFSharp Notebook

The Choices I have experimented with OpenCV and Dlib face detection in my computer vision pipeline. Both work well, but the Dlib one worked better: it is more sensitive with (in my case) almost no false positives right out of the box! Dlib uses several HOG filters that account for profile as well as frontal … Continue reading Detecting Faces with Dlib from F#. IFSharp Notebook →

Getting Emotional with Affectiva, F#, and Emgu

I've been playing with Affectiva emotion, demographics, and face detection SDK, found it excellent, however, their sample gallery lacks a sample in F#! So here we are to correct that. I just wanted a simple F# script that would let me take all kinds of the SDK options for a ride. The script itself is … Continue reading Getting Emotional with Affectiva, F#, and Emgu →