ChenJiaDong9219

MotionPlanning. Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)

PathPlanning. Common used path planning algorithms with animations.

easy-rl. 强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/

movieRecommendation. 本项目使用两种算法来实现一个电影推荐系统，一个是CNN，另一个是矩阵分解的协同过滤。

mall. mall项目是一套电商系统，包括前台商城系统及后台管理系统，基于Spring Boot+MyBatis实现，采用Docker容器化部署。 前台商城系统包含首页门户、商品推荐、商品搜索、商品展示、购物车、订单流程、会员中心、客户服务、帮助中心等模块。 后台管理系统包含商品管理、订单管理、会员管理、促销管理、运营管理、内容管理、统计报表、财务管理、权限管理、设置等模块。

tensor2tensor. Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

AudioSegment. Wrapper for pydub AudioSegment objects

Face-Detect. A Python based tool to extract faces from any picture.

keras-contrib. Keras community contributions

MMdnn. MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

deep-learning-model-convertor. The convertor/conversion of deep learning models for different deep learning frameworks/softwares.

Human-Action-Recognition-with-Keras. Keras implementation of Human Action Recognition for the data set State Farm Distracted Driver Detection (Kaggle)

py-denseflow. Extract TVL1 optical flows in python (multi-process && multi-server)

keras_models. Deep Nets written using the Keras framework.

cloudml-samples. Cloud ML Engine repo. Please visit the new Vertex AI samples repo at https://github.com/GoogleCloudPlatform/vertex-ai-samples

two-stream-action-recognition. Using two stream architecture to implement a classic action recognition method on UCF101 dataset

musicinformationretrieval.com. Instructional notebooks on music information retrieval.

HappyNet. Convolutional neural network that does real-time emotion recognition. HappyNet detects faces in video and images, classifies the emotion on each face, then replaces each face with the correct emoji for that emotion. Based on Caffe and the "Emotions in the Wild" network available on Caffe model zoo.

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.

Facial-Expression-Recognition. Facial-Expression-Recognition in TensorFlow. Detecting faces in video and recognize the expression(emotion).

Emotion-Recognition-RNN. Recurrent Neural Networks for Emotion Recognition in Video

iGAN. Interactive Image Generation via Generative Adversarial Networks