wkecn's starred repositories
Pytorch-UNet
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
imageRetrieval
Image retrieval learning record
FFMPEG-study
学习FFMPEG的一些代码示例
keras-resnet3d
Implementations of ResNets for volumetric data, including a vanilla resnet in 3D.
3D-ConvNets-for-Action-Recognition
3D ConvNets for Action Recognition with Keras (3d ResNet, 3d DenseNet, 3d Inception, C3D, 3d dense resnet)
pretorched-x
Pretrained Image & Video ConvNets and GANs for PyTorch: NASNet, ResNeXt (2D + 3D), ResNet (2D + 3D), InceptionV4, InceptionResnetV2, Xception, DPN, NonLocalNets, R(2+1)D nets, MultiView CNNs, Temporal Relation Networks, BigGANs StyleGANs, etc.
two-stream-pytorch
PyTorch implementation of two-stream networks for video action recognition
awesome-action-recognition
A curated list of action recognition and related area resources
c3d-pytorch
Pytorch porting of C3D network, with Sports1M weights
3D-ResNets-PyTorch
3D ResNets for Action Recognition (CVPR 2018)
tensorflow-windows-wheel
Tensorflow prebuilt binary for Windows
five-video-classification-methods
Code that accompanies my blog post outlining five video classification methods in Keras and TensorFlow
discourse-ablstm
Attention-based Bidirectional Long Short-Term Memory neural network for classification of Chinese implicit discourse relations
Compute-Features
Computes features for images using various pretrained Tensorflow models
Youtube-Video-Label-Classification
Youtube Video Label Classification using Single Frame model and Long-term Recurrent Convolutional Networks (LRCN) model
Video-Classification
Bidirectional LSTM with zoneout and layer normalization for Youtube 8m video classification
UCF101-Classification
Analyze and classify videos in the UCF101 dataset
video-classification
TensorFlow implementation for video classification.
video-action-classification
Video Action Classification Using Spatial Temporal Clues. Original paper: arXiv:1504.01561
tensorflow-video-classifier
image classification via video input, frame-by-frame
Emotion-Detection-in-Videos
The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.
fisher_vectors
Fisher vectors for video classification