KickView Tech Blog

kvSonata

IR Facial Detection and Recognition

Facial detection and recognition is being integrated into devices we use everyday, from cell phones to ATM machines. What happens though, when the lights go out?

intelligent video analytics

Real-Time Intelligent Video Analytics For Your Applications

If your mission is to deploy effective edge processing solutions while having direct access to expert machine learning resources and support, then this blog post is for you.

AI

No GPU Required: Real-Time Inference with Optimized Networks in kvSonata

The rise of deep learning ran in parallel with advancements in GPU technology. As GPUs got faster, mostly fueled by the high demand for powerful graphics cards in the world of gaming, researchers started to train the high-capacity neural networks that quickly evolved into the well known CNN architectures of

smart parking

KickView Smart Parking Application

Drivers have more options to consume transportation related services. However, on a daily basis, too many people are dealing with the hassles of finding appropriate parking. Therefore, one service that can greatly benefit many people is a streamlined parking solution.

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 and often force your solutions into expensive hardware and software configurations. Therefore, KickView has created a new

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 we turn our attention to accuracy metrics. We will examine some commonly used metrics such as mean

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 in the problem of object tracking in video data. Advances in deep learning have opened doors to

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 we summarized the time saved by using a DGX for various training tasks. In this post we

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 can take hours, days, or even sometimes weeks depending on the complexity of the model, the amount

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 a vehicle, pedestrian, bicycle, etc.). Similar to traditional computer vision systems, bounding boxes can be placed around

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 for learning how to use Deep Learning to process imagery. However, few resources exist to demonstrate how

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 being able to use it on a mobile device only unlocks more of it's potential. Most of

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 NVIDIA DIGITS application as a learning tool. This application is a convenient tool for training, tuning, and

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, or radar. In this blog post, we will introduce some basic methods for utilizing a Convolutional Neural

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 over where and what gets installed onto your dev machine, which also mean that sometimes, it's worth