Aircraft tracking (CV Project final part)

In this article, I’m going to present the final part of our computer vision project namely moving object detection, tracking, positioning, and speed estimation using only a single camera. This part contains an example of ‘visual aircraft tracking’. The project has been finally completed using python and opencv library. After giving the required reference libraries […]

Custom object tracker (CV Project part 4)

Visual object trackers are commonly used in computer vision applications. The goal of a tracker is to estimate the location of a target of interest in each frame of an image sequence. There are plenty of trackers in the literature. Some of them are based on convolutional neural networks (CNN). It has been shown that […]

Kalman filter for visual tracking (CV Project part 2)

In this article, I’m going to present the details of Kalman filtering which is one of the most important subjects in engineering. It is also essential for our computer vision project. After giving some information about the method, I will share the python codes at the end. In this project, we will use the Kalman […]

Object detection, tracking, and 3D positioning using a single camera

In this article, I’m going to share some results of my computer vision project namely moving object detection, tracking, 3D positioning, and speed estimation using only a single camera. The project has been completed using opencv and python. This post will summarize the project and in the upcoming posts I will present the details step […]

Deep Q-learning for algorithmic trading

In this post, I’m going to briefly present the deep Q-learning method which is the combination of reinforcement learning (RL) and deep neural networks. After that, I’m going to show how I applied this technique to algorithmic trading. At the end of the post, I’m going to share the python implementation of the training procedure. […]

Creating a custom gym (OpenAi) environment for algorithmic trading

Gym is a library which provides environments for the reinforcement learning algorithms. There are plenty of environments included in the library such as classic control, 2D and 3D robots, and atari games. An environment includes some public functions containing ‘step’, ‘reset’, and ‘render’. When the ‘step’ function is called, it returns four values, namely ‘obervation’, […]