Easy to use video deep features extractor

Video Feature Extraction Code for EMNLP 2020 paper "HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training"

Feature Extractor module for videos using the PySlowFast framework

To provide the stego community with C/C++ implementations of selected feature extractors mainly targeted at H.264 steganography.

A repository for extract CNN features from videos using pytorch

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Video Feature Extractor for S3D-HowTo100M

Feature extraction and feature extractor training for Soccernet videos.

Python implementation of extraction of several visual features representations from videos

A simple and effective feature extractor for untrimmed videos

Classifying skateboard tricks from video clips using DeepMind's I3D model and an audio feature extractor..

A simple and fun video classification/action recognition using VGG16 as a feature extractor and RNN.

Video feature extractor in PyTorch.

A handy script for feature extraction using VideoMAE

Video Extractor to h5 File Format

Feature extractor for the PySlowFast framework. Using Decord the code gets video-action features from a folder of videos.

Sign language interpreters are currently required for interpreting the speech impaired people. This skill-based job of interpreters is cumbersome and hence the number of interpreters per capita across majority countries are very low or decreasing. We aim to harness technology in developing a powerful continuous sign language gestures recognition system. This computer vision-based approach will be used to recognise Argentinian sign language gestures from a video. Translating these sign language gestures is considered a monumental task in this field. The project proposes to investigate whether sign language gestures can be recognised by using a trained modified Inception V3 working as a feature selector and classifier, with a LTSM Recurrent Neural Network. Two separate approaches have been applied to recognise the Argentinian gestures. The Global Max Pooling approach outperforms the SoftMax approach, with a model accuracy of 86.10% on validation set and 75.2% on test set. Using the Inception V3 model as a feature extractor for LTSM RNN worked more efficiently and produced better results than using the Inception V3 model as a classifier. These results show the effectiveness of the research conducted. This research will help in classifying and recognising continuous sign language gestures based on machine vision. This in turn will assist people that are affected by speech and hearing impairment in understanding, translating and recognising sign gestures.

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Dealing with videos for classification can be difficult as there's both features across space as well as across time. Using a CNN based feature extractor would extract the spacial features and let an RNN/ LSTM take care of the temporal features. This repo contains code to extract the spatial features using pretrained networks.

This program uses the OpenCV libraries to perform feature detection, description, and matching between two sets of images, or an image and a video feed, or between two video feeds. It has a GUI made with QT to allow the user to toggle the parameters of the Feature extractors and descriptors in real time, and to change from extractor to extractor. This allows for quick testing of different Computer Vision algorithms in real time on different sets of images, without the user having to enter any code.

A video feature extractor using the epic fusion model

It is a homework of "Document Reading". It summary some typical deep structure about video feature extractor.

Extract video features using C3D feature extractor from Facebook with trained weights available.

Monocular Visual Odometry implementation using the XFeat deep learning feature extractor on the KITTI dataset, featuring trajectory visualization, video generation, and RMSE evaluation.

Using one image to reflect its artistic style on the contents of the frames in the video stream. Implemented in Python using a pretrained Squeezenet model as a feature extractor.

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A Python tool for automated video feature extraction using OpenCV, Optical Flow, and OCR. Detects hard cuts, measures motion intensity, and identifies on-screen text which enables intelligent video analysis, summarization, and content understanding.

Extract features (I3D, Resnet) for 20bn-Somtething-Something datasets

Video feature tracker and extractor in Visual Slam

Initial Idea of a scene based video feature extractor focused on CATS