KickView Tech Blog

intelligent video analytics

Real-Time Intelligent Video Analytics for Advanced Traffic Management

Your video sensor data contains a tremendous amount of valuable information. Additionally, your operators understand the variables, behaviours, and constraints that best drive your system. However, existing video analytics software applications are rigid

video analytics

Create a Streaming AI Video Tiler in 15 Minutes Using kvSonata

A common application for real-time streaming video is the video tiler. With kvSonata you can configure and customize a video tiler in 15 minutes or less.

Real-Time AI

Easy-to-Use Real-Time AI Framework

The team at KickView is very excited to announce the release of our easy-to-use, real-time AI video and multi-sensor processing framework, called kvSonata!

Deep Learning

Deep Learning Meets DSP: OFDM Signal Detection

In this blog post, we'll focus specifically on detection of RF signals modulated using Orthogonal Frequency Division Multiplexing (OFDM) using deep learning.

Deep Learning

Choosing a Convolutional Neural Network Architecture for Real-Time Object Tracking (Part 2)

This is part 2 of 3 in a series about selecting appropriate network architectures for real-time object tracking. In part 1 we compared the inference speed of various existing object detection networks. Now

Deep Learning

Choosing a Convolutional Neural Network Architecture for Real-Time Object Tracking (Part 1)

In a previous blog post we talked about how to train a convolutional neural network(CNN) for object detection in images. Object detection combines classification and localization. One use for object detection is

Deep Learning

Intelligent RF Spectral Monitoring

In this blog, we highlight a number of different spectral monitoring use cases where deep learning can play a key role and show how intelligent spectral monitoring can be applied to a number of different use-cases in the RF domain.

DGX

Getting Python 3 running on a DGX

We recieved a DGX station a few weeks ago and it is a powerhouse. We will not dive into the specs of the machine since it was covered in a previous post, where

DGX

The DGX has arrived!

If you've ever had the opportunity to train a neural network, you've probably realized that it's a major time saver to start with weights from a pre-trained model. Training a model from scratch

Signals

Real-World RF Processing: Theory, Challenges & Machine Learning

Understanding the theory and processing techniques from the digital communications world is critical in order to apply any sort of machine learning to the RF domain.

background subtraction

Background Subtraction

We'll show how to actually generate an estimate of the background, and then use it to perform detection on moving objects.

Deep Learning

Modulation Recognition Using Deep Learning

In this blog post, we'll talk about how to apply deep learning to modulation recognition, a challenging problem that has many applications in the digital communications world.

Deep Learning

Training an FCN for Object Detection

One of the many useful tasks that can be accomplished using deep learning is visual object detection. For example, a Deep Neural Network (DNN) can be trained to detect an object (such as

Signal Detection Using Deep Learning - Part II

In the previous post, Signal Detection Using Deep Learning - Part I, we introduced basic methods for detecting signals corrupted by noise. The motivation for these posts is that there are many resources

OpenCV

OpenCV 3.2.0 Setup on iOS

Gone are the days of needing a dedicated desktop to be able to utilize the power of image processing software. OpenCV is one of the most popular image processing libraries out there, so

Deep Learning

Training A CNN With The CIFAR-10 Dataset Using DIGITS 4.1

Recently, several friends and contacts have expressed an interest in learning about deep learning and how to train a neural network. Although there are many resources available, I usually point them towards the

Convolutional Neural Network

Signal Detection Using Deep Learning

There are many resources for learning how to use Deep Learning to process imagery. However, very few resources exist to demonstrate how to process data from other sensors such as acoustic, seismic, radio,

DIGITS

Building a Digits Dev Machine on Ubuntu 16.04

Ubuntu 16.04 with CUDA 8.0, OpenCV 3.2.0, Caffe 0.15, and Digits 5.0 ==Notes: Updated: 6/22/2017 == Pre-Setup If you're like me, you like to have control