huyusheng123's starred repositories
Recent_SLAM_Research
Track Advancement of SLAM 跟踪SLAM前沿动态【2021 version】業務調整，暫停更新
semantic_suma
SuMa++: Efficient LiDAR-based Semantic SLAM (Chen et al IROS 2019)
siamese-fc
Arbitrary object tracking at 50-100 FPS with Fully Convolutional Siamese networks.
SqueezeSeg_Ros
This is a ros package that implement the SqueezeSeg
VoxelNetRos
implement the VoxelNet with ROS, using Kitti data to test
GMS-Feature-Matcher
GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence (CVPR 17 & IJCV 20)
multi-object-tracking-paper-list
Paper list and source code for multi-object-tracking
Lidar_For_AD_references
A list of references on lidar point cloud processing for autonomous driving
Deep-Learning-for-Tracking-and-Detection
Collection of papers, datasets, code and other resources for object tracking and detection using deep learning
awesome-point-cloud-analysis
A list of papers and datasets about point cloud analysis (processing)
rtabmap_ros
RTAB-Map's ROS package.
EmptyCities
Implementation for learning a mapping from images that contain dynamic objects in a city environment to static realistic images
image-analogies
Generate image analogies using neural matching and blending.
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.
image-registration
A MATLAB library/toolbox providing access to image registration suitable for use with medical images.
Image-Registration
Image registration algorithm. Includes SIFT, SAR-SIFT,PSO-SIFT.
detectAndTrack
moving object detection and tracking
Moving-Object-Detection-MOD-
SImply RANSAC find fundamental matrix method is applied to detect moving objects even when camera is under motion. Undistortion can be done in camera set of either fisheye or normal pinhole model.
V2V-PoseNet_RELEASE
Official Torch7 implementation of "V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map", CVPR 2018
adaptive_clustering
[ROS package] Lightweight and Accurate Point Cloud Clustering
Run_based_segmentation
An ongoing implementation ros node on `fast segmentation of 3d point clouds: a paradigm`...