sseshadr Goto Github PK
Name: Swarooph Nirmal Seshadri
Type: User
Bio: Also as @swaroophs. This is my personal account.
Swarooph Nirmal Seshadri's Projects
auvsi-cv-all
All files for the online Computer Vision Training for AUVSI Foundation Teams
auvsi-cv-linedetection
Learn how to detect lines using MATLAB. The concept of a Hough Transform is demonstrated to show how to use them to extract line segments. Tips on some preprocessing techniques are also provided to improve line detection results. An example of lane detection is used to explain these concepts.
auvsi-cv-motionestimation
Learn how we perceive motion and how to estimate motion using a technique called Optical Flow. You can use three algorithms to implement optical flow using the Computer Vision Toolbox. These three algorithms are Horn-Schunck method, Farneback method, and Lucas-Kanade method. An example of a robot boat moving through a field of buoys will be used.
auvsi-cv-objecttracking
Learn how to track an object across video frames. Object tracking using histogram based tracking, tracking occluded or hidden objects using a Kalman Filter, and multiple objects tracking are covered. An example of tracking a moving ball will be used.
auvsi-cv-pointcloud
Learn how to read, load and visualize point clouds using MATLAB and pre-process the data by down sampling and de-noising. You will also learn how to apply affine transforms like translation and rotation. Finally, you will learn how to fit point clouds to geometric shapes and how to extract a region of interest from images using point clouds.
auvsi-cv-stereovision
Learn how to import and display stereo vision images. Also understand how to calibrate stereo cameras, rectify images to align them horizontally, generate disparity maps and create point clouds with scene reconstruction.
carnd-advanced-lane-lines
Identify lane boundaries through camera calibration, thresholding, perspective transform and polynomial fitting.
carnd-behavioral-cloning-p3
Drive a simulated car autonomously using a deep learning network implemented with Keras.
carnd-extended-kalman-filter
Use Kalman filter principles to fuse LiDAR and RADAR sensor data to provide a more accurate estimate of a simulated vehicle's position and velocity.
carnd-kidnapped-vehicle-project
Use a particle filter to localize a vehicle based on existing landmark positions.
carnd-lanelines-p1
This project uses traditional computer vision techniques to detect lanes.
carnd-mpc-project
Use MPC control to drive a simulated car on its track. Perform the constrained optimization problem in the loop to compute actuator values.
carnd-path-planning-project
Create a path planner that is able to generate and follow a trajectory in a virtual highway scenario. Perform safe lane changes as necessary and travel near speed limit for as long as possible.
carnd-pid-control
Use a PID control to drive a simulated car on its track. Perform parameter tuning using the Twiddle logic.
carnd-semantic-segmentation-project
Create a semantic segmentation network that is able to label individual pixels. This particular project labels road pixels.
carnd-traffic-sign-classifier-project
Recognizing German Traffic and Road signs using TensorFlow Deep Learning Framework in Python
carnd-unscented-kalman-filter
Use Unscented Kalman filter principles with the CTRV model to fuse LiDAR and RADAR sensor data to provide a more accurate estimate of a simulated vehicle's position and velocity.
carnd-vehicle-detection
Detect cars in a video frame using color, spatial and HoG feature extraction and a traditional machine learning classifier model such as SVM.
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