Pinned Repositories
CDC-Press-Release-Crawler
Deep-Feature-Encoding-with-MNIST-data
UMAP is used to present the difference between the original MNIST data and its encoded features.
Feature-tracking-with-PCA
Traditional feature tracking techniques such as SIFT, SURF, and Lucas Kanade algorithms define key points in terms of finding poles and cannot specify specific tracking points. The general Deep Learning based tracking algorithms such as Siamese tracker require a lot of resources for neural network training. Here, we implement feature tracking using PCA. Our algorithm can specify tracking points and does not require extensive training.
Muscle-enhancing-via-Autoencoder
This is just a funny project that we want to see AutoEncoder (AE) can actually work to enhance the features we want. We will start to improve...
My-career-as-a-software-engineer
Presentation of the results of in-service projects
Pixel-scanning-removal-Hands11k-dataset
Python-execute-MATLAB-code
A simple tutorial which teaches you how to call MATLAB in python.
Real-Time-Yolov8-Pose-tracking-Sequence
Self-adaptive-Feature-tracking-with-PCA-Lucas-Kanade
The inability to change size has always been a drawback of sliding window tracking. If the previous frame of the current frame is used as the reference frame, the error rate is often superimposed. If only traditional feature tracking methods such as SIFT, SURF or Lucas-Kanade are used, it is not possible to track a specific object and there is no defined object frame to define the overall features of the object to be tracked. Using Deep Learning (DL) for object tracking such as Siamese Tracker requires training of the object to be tracked, and the size of the tracking bounding box cannot be defined arbitrarily while tracking. We propose to use Principal Component Analysis (PCA) as the feature extraction mechanism and Lucas-Kanade (LK) tracking optical flow as the object size prediction: 1. no time-consuming DL training is required for the objects. 2. 2. The object frame size can be defined arbitrarily. 3. 3. Automatically detects and adjusts object size even if it changes.
VideoAddSubTitles_with_whisper
End to end add (translated) subtitles on video file with whisper which developed by OpenAI.
JacobChen1998's Repositories
JacobChen1998/Real-Time-Yolov8-Pose-tracking-Sequence
JacobChen1998/Feature-tracking-with-PCA
Traditional feature tracking techniques such as SIFT, SURF, and Lucas Kanade algorithms define key points in terms of finding poles and cannot specify specific tracking points. The general Deep Learning based tracking algorithms such as Siamese tracker require a lot of resources for neural network training. Here, we implement feature tracking using PCA. Our algorithm can specify tracking points and does not require extensive training.
JacobChen1998/Self-adaptive-Feature-tracking-with-PCA-Lucas-Kanade
The inability to change size has always been a drawback of sliding window tracking. If the previous frame of the current frame is used as the reference frame, the error rate is often superimposed. If only traditional feature tracking methods such as SIFT, SURF or Lucas-Kanade are used, it is not possible to track a specific object and there is no defined object frame to define the overall features of the object to be tracked. Using Deep Learning (DL) for object tracking such as Siamese Tracker requires training of the object to be tracked, and the size of the tracking bounding box cannot be defined arbitrarily while tracking. We propose to use Principal Component Analysis (PCA) as the feature extraction mechanism and Lucas-Kanade (LK) tracking optical flow as the object size prediction: 1. no time-consuming DL training is required for the objects. 2. 2. The object frame size can be defined arbitrarily. 3. 3. Automatically detects and adjusts object size even if it changes.
JacobChen1998/Deep-Feature-Encoding-with-MNIST-data
UMAP is used to present the difference between the original MNIST data and its encoded features.
JacobChen1998/Muscle-enhancing-via-Autoencoder
This is just a funny project that we want to see AutoEncoder (AE) can actually work to enhance the features we want. We will start to improve...
JacobChen1998/CDC-Press-Release-Crawler
JacobChen1998/Pixel-scanning-removal-Hands11k-dataset
JacobChen1998/Python-execute-MATLAB-code
A simple tutorial which teaches you how to call MATLAB in python.
JacobChen1998/Involution_Example_MNIST
A simple example that uses involution layer instead of convolution layer in MNIST classification task.
JacobChen1998/Scan-based-Feature-tracking-C-
This is the new repository that is same concept as my previous project " Feature-tracking-with-PCA " but be written in C++
JacobChen1998/VideoAddSubTitles_with_whisper
End to end add (translated) subtitles on video file with whisper which developed by OpenAI.
JacobChen1998/My-career-as-a-software-engineer
Presentation of the results of in-service projects
JacobChen1998/simple-implement
This repository contains some simple implementations, and I hope they will be helpful to everyone.
JacobChen1998/Streaming-with-Django-and-OpenCV
JacobChen1998/Django-E-commerce-Website
E-commerce Website Developed Using the Django Framework in Python
JacobChen1998/emoji-cheat-sheet
A markdown version emoji cheat sheet
JacobChen1998/Human-Falling-Detect-Tracks
AlphaPose + ST-GCN + SORT.
JacobChen1998/iou-tracker
Python implementation of the IOU Tracker