In this repository I will give some implementation of single and multiple object tracking algorithms. These include meanShift, CamShift, Boosting, MIL, KCF, TLD , GoTurn, and MedianFlow. Additionally I will show you how to grab frames at a very high FPS from camera and videos.

Face Detection and tracking using CamShift, Kalman Filter, Optical Flow

Computer Vision model to detect face in the first frame of a video and to continue tracking it in the rest of the video. This is implemented in OpenCV 3.3.0 and Python 2.7

Autonomous drone using detected ball to command the direction of the drone

Various projects using Open CV

YOLOv5+DeepSORT and Camshift+Kalman

Face Detection and tracking using CamShift, Kalman Filter, Optical Flow

Multi-Agent Visual Tracking Project based on CamShift

C++ refinement of PedestrianCounter.

Lucas-Kanade & Farneback optical flow, MeanShift/CAMShift, and OpenCV KCF/MIL trackers

Tracking object from video using cv2

Object Tracking

Using the meanshift and optical flow algorithm to trackt objects in RTSP video streams and mp4 video files

A practical portfolio project demonstrating implementations of computer vision techniques with OpenCV. Features: Optical Flow (Sparse/Dense), Background Subtraction (MOG2), and Object Tracking (Meanshift, Camshift, GOTURN, CSRT Multi-Tracker).

OpenCV demonstrations of selected algorithms related to motion tracking: (1) mean shift, (2) CAMSHIFT, (3) iterative Lucas-Kanade algorithm with pyramids, and (4) Farnebäck's algorithm

Computer Vision model to detect face in the first frame of a video and to continue tracking it in the rest of the video. This is implemented in OpenCV 3.3.0 and Python 2.7

base on opencv write in python2.7

homework；course：Image acquisition and processing；goal：tracking a colorful object

CAMSHIFT, a colour based tracking system, and a Non-Photorealistic Rendering application. Created during 2013 Nuffield Placement at Glasgow Caledonian University.