This work proposes a supervised learning technique to control active cameras with Deep Convolutional Neural Networks to go directly from visual information to camera movement.

Active Camera Systems

Active vision systems (i.e., movable cameras with controllable parameters such as pan and tilt) can provide extended coverage, flexibility, and cost-efficiency, compared to static vision…

“The flexibility and cost efficiency of traffic monitoring using Unmanned Aerial Vehicles (UAVs) has made such a proposition an attractive topic of research.”


Unmanned Aerial Vehicles (drones) are emerging as a promising technology for both environmental and infrastructure monitoring, with broad use in a plethora of applications. Many such applications…

Keras is a useful API for deep learning that also includes various pretrained models that you can used for transfer learning.

UPDATE! Now works with tf.keras!

The Keras API

Keras is a high level API (Application Programming Interface) for deep learning. That is it does not itself implement deep learning functionality but is…

Deep learning approaches have demonstrated state-of-the-art performance in various computer vision tasks such as object detection and recognition. In this post I provide details on how to develop and train a Convolutional Neural Network (CNN) to detect top-view vehicles from UAV footage.


Unmanned Aerial Vehicles (drones) are emerging as a…

The objective of this project is to label pixels corresponding to road in images using a Fully Convolutional Network (FCN).


This specific module was a collaboration between UDACITY and NVIDIAs Deep Learning Institute. This module covers semantic segmentation, and inference optimization.

Semantic segmentation identifies free space on the road at…

The goal of this project was to design a path planner that is able to create smooth, safe paths for the car to follow along a 3-lane highway with traffic. …

LBPs are local patterns that describe the relationship between a pixel and its neighborhood.

Local Binary Patterns (LBPs) have been used for a wide range of applications ranging from face detection [1], [2], face recognition [3], facial expression recognition [4], pedestrian detection [5], to remote sensing and texture classification [6]…

Filtering and feature extraction are both very important tasks for efficient object recognition in embedded vision systems.

Perhaps one of the simplest, but also effective, forms of filtering is using color information which can be a very important factor in recognizing and detecting specific objects. For example, if you are…

“Parallel computing” stands for the ability of computer systems to perform multiple operations simultaneously. The main driver behind parallel computing is the fact that large problems can be divided to smaller ones which can be then solved in parallel — i.e. executed concurrently on the available computing resources.


The quest…

Object detection deals with determining whether an object of interest is present in an image/video frame or not. It is a necessary task for embedded vision systems as it enables them to interact more intelligently with their host environment, and increases their responsiveness and awareness with regards to their surroundings.

Christos Kyrkou

PhD in Computer Engineering, Self-Driving Car Engineering Nanodegree, Computer Vision, Visual Perception and Computing

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