Gesture recognition using a depth camera for human robot collaboration on assembly line

International audienceWe present a framework and preliminary experimental results for technical gestures recognition using a RGB-D camera. We have studied a collaborative task between a robot and an operator: the assembly of a motor hoses. The goal is to enable the robot to understand which task has just been executed by a human operator in order to anticipate on his actions, to adapt his speed and react properly if an unusual event occurs. The depth camera is placed above the operator, to minimize the possible occlusion on an assembly line, and we track the head and the hands of the operator using the geodesic distance between the head and the pixels of his torso. To describe his movements we used the shape of the shortest routes joining the head and the hands. We then used a discreet HMM to learn and recognize five gestures performed during the motor hoses assembly. By using gesture from the same operator for the learning and the recognition, we reach a good recognition rate of 93%. These results are encouraging and ongoing work will lead us to experiment our set up on a larger pool of operators and recognize the gesture in real time

Multi-frame scene-flow estimation using a patch model and smooth motion prior

This paper addresses the problem of estimating the dense 3D motion of a scene over several frames using a set of calibrated cameras. Most current 3D motion estimation techniques are limited to estimating the motion over a single frame, unless a strong prior model of the scene (such as a skeleton) is introduced. Estimating the 3D motion of a general scene is difficult due to untextured surfaces, complex movements and occlusions. In this paper, we show that it is possible to track the surfaces of a scene over several frames, by introducing an effective prior on the scene motion. Experimental results show that the proposed method estimates the dense scene-flow over multiple frames, without the need for multiple-view reconstructions at every frame. Furthermore, the accuracy of the proposed method is demonstrated by comparing the estimated motion against a ground truth

Novel Correspondence-based Approach for Consistent Human Skeleton Extraction

This paper presents a novel base-points-driven shape correspondence (BSC) approach to extract skeletons of articulated objects from 3D mesh shapes. The skeleton extraction based on BSC approach is more accurate than the traditional direct skeleton extraction methods. Since 3D shapes provide more geometric information, BSC offers the consistent information between the source shape and the target shapes. In this paper, we first extract the skeleton from a template shape such as the source shape automatically. Then, the skeletons of the target shapes of different poses are generated based on the correspondence relationship with source shape. The accuracy of the proposed method is demonstrated by presenting a comprehensive performance evaluation on multiple benchmark datasets. The results of the proposed approach can be applied to various applications such as skeleton-driven animation, shape segmentation and human motion analysis

LEARNING TO RIG CHARACTERS

With the emergence of 3D virtual worlds, 3D social media, and massive online games, the need for diverse, high-quality, animation-ready characters and avatars is greater than ever. To animate characters, artists hand-craft articulation structures, such as animation skeletons and part deformers, which require significant amount of manual and laborious interaction with 2D/3D modeling interfaces. This thesis presents deep learning methods that are able to significantly automate the process of character rigging. First, the thesis introduces RigNet, a method capable of predicting an animation skeleton for an input static 3D shape in the form of a polygon mesh. The predicted skeletons match the animator expectations in joint placement and topology. RigNet also estimates surface skin weights which determine how the mesh is animated given the different skeletal poses. In contrast to prior work that fits pre-defined skeletal templates with hand-tuned objectives, RigNet is able to automatically rig diverse characters, such as humanoids, quadrupeds, toys, birds, with varying articulation structure and geometry. RigNet is based on a deep neural architecture that directly operates on the mesh representation. The architecture is trained on a diverse dataset of rigged models that we mined online and curated. The dataset includes 2.7K polygon meshes, along with their associated skeletons and corresponding skin weights. Second, the thesis introduces Morig, a method that automatically rigs character meshes driven by single-view point cloud streams capturing the motion of performing characters. Compared to RigNet, MoRig\u27s rigging is \emph{motion-aware}: its neural network encodes motion cues from the point clouds into compact feature representations that are informative about the articulated parts of the performing character. These motion-aware features guide the inference of an appropriate skeletal rig for the input mesh. Furthermore, Morig is able to animate the rig according to the captured point cloud motion. Morig can handle diverse characters with different morphologies (e.g., humanoids, quadrupeds, toy characters). It also accounts for occluded regions in the point clouds and mismatches in the part proportions between the input mesh and captured character. Third, the thesis introduces APES, a method that takes as input 2D raster images depicting a small set of poses of a character shown in a sprite sheet, and identifies articulated parts useful for rigging the character. APES uses a combination of neural network inference and integer linear programming to identify a compact set of articulated body parts, e.g. head, torso and limbs, that best reconstruct the input poses. Compared to Morig and RigNet that require a large collection of training models with associated skeletons and skinning weights, APES\u27 neural architecture relies on less effortful supervision from (i) pixel correspondences readily available in existing large cartoon image datasets (e.g., Creative Flow), (ii) a relatively small dataset of 57 cartoon characters segmented into moving parts. Finally, the thesis discusses future research directions related to combining neural rigging with 3D and 4D reconstruction of characters from point cloud data and 2D video as well as automating the process of motion synthesis for 3D characters

Accurate Joint Detection from Depth Videos towards Pose Analysis

Joint detection is vital for characterizing human pose and serves as a foundation for a wide range of computer vision applications such as physical training, health care, entertainment. This dissertation proposed two methods to detect joints in the human body for pose analysis. The first method detects joints by combining body model and automatic feature points detection together. The human body model maps the detected extreme points to the corresponding body parts of the model and detects the position of implicit joints. The dominant joints are detected after implicit joints and extreme points are located by a shortest path based methods. The main contribution of this work is a hybrid framework to detect joints on the human body to achieve robustness to different body shapes or proportions, pose variations and occlusions. Another contribution of this work is the idea of using geodesic features of the human body to build a model for guiding the human pose detection and estimation. The second proposed method detects joints by segmenting human body into parts first and then detect joints by making the detection algorithm focusing on each limb. The advantage of applying body part segmentation first is that the body segmentation method narrows down the searching area for each joint so that the joint detection method can provide more stable and accurate results

Multi-Modality Human Action Recognition

Human action recognition is very useful in many applications in various areas, e.g. video surveillance, HCI (Human computer interaction), video retrieval, gaming and security. Recently, human action recognition becomes an active research topic in computer vision and pattern recognition. A number of action recognition approaches have been proposed. However, most of the approaches are designed on the RGB images sequences, where the action data was collected by RGB/intensity camera. Thus the recognition performance is usually related to various occlusion, background, and lighting conditions of the image sequences. If more information can be provided along with the image sequences, more data sources other than the RGB video can be utilized, human actions could be better represented and recognized by the designed computer vision system.;In this dissertation, the multi-modality human action recognition is studied. On one hand, we introduce the study of multi-spectral action recognition, which involves the information from different spectrum beyond visible, e.g. infrared and near infrared. Action recognition in individual spectra is explored and new methods are proposed. Then the cross-spectral action recognition is also investigated and novel approaches are proposed in our work. On the other hand, since the depth imaging technology has made a significant progress recently, where depth information can be captured simultaneously with the RGB videos. The depth-based human action recognition is also investigated. I first propose a method combining different type of depth data to recognize human actions. Then a thorough evaluation is conducted on spatiotemporal interest point (STIP) based features for depth-based action recognition. Finally, I advocate the study of fusing different features for depth-based action analysis. Moreover, human depression recognition is studied by combining facial appearance model as well as facial dynamic model