Erwan Guillou

Ongoing human action recognition with motion capture

Ongoing human action recognition is a challenging problem that has many applications, such as video surveillance, patient monitoring, human-computer interaction, etc. This paper presents a novel framework for recognizing streamed actions using Motion Capture (MoCap) data. Unlike the after-the-fact classification of completed activities, this work aims at achieving early recognition of ongoing activities. The proposed method is time efficient as it is based on histograms of action poses, extracted from MoCap data, that are computed according to Hausdorff distance. The histograms are then compared with the Bhattacharyya distance and warped by a dynamic time warping process to achieve their optimal alignment. This process, implemented by our dynamic programming-based solution, has the advantage of allowing some stretching flexibility to accommodate for possible action length changes. We have shown the success and effectiveness of our solution by testing it on large datasets and comparing it with several state-of-the-art methods. In particular, we were able to achieve excellent recognition rates that have outperformed many well known methods. HighlightsHuman motion interpretation from Motion Capture systems.Histogram of poses analysis.Ongoing activity recognition.

Real-Time Marker-free Motion Capture from multiple cameras

We present a fully-automated method for real-time and marker-free 3D human motion capture. The system computes the 3D shape of the person filmed from a synchronized camera set. We obtain a robust and real-time system by using both a fast 3D shape analysis and a skin segmentation algorithm for human tracking. A skeleton-based approach facilitates the shape analysis. We are able to track fast and complex human motion in very difficult cases, like self-occlusion. Results on long video sequences with rapid and complex movements, demonstrate our approach robustness.

Real-time and markerless 3D human motion capture using multiple views

We present a fully automated system for real-time markerless 3D human motion capture. Our approach, based on fast algorithms, uses simple techniques and requires low-cost devices. Using input from multiple calibrated webcams, an extended Shape-From-Silhouette algorithm reconstructs the person in real-time. Fast 3D shape and 3D skin parts analysis provide a robust and real-time system for human full-body tracking. Animation skeleton and simple morphological constraints make easier the motion capture process. Thanks to fast and simple algorithms and low-cost cameras, our system is perfectly apt for home entertainment device. Results on real video sequences with complicated motions demonstrate the robustness of the approach.

A real-time system for motion retrieval and interpretation

This paper proposes a new examplar-based method for real-time human motion recognition using Motion Capture (MoCap) data. We have formalized streamed recognizable actions, coming from an online MoCap engine, into a motion graph that is similar to an animation motion graph. This graph is used as an automaton to recognize known actions as well as to add new ones. We have defined and used a spatio-temporal metric for similarity measurements to achieve more accurate feedbacks on classification. The proposed method has the advantage of being linear and incremental, making the recognition process very fast and the addition of a new action straightforward. Furthermore, actions can be recognized with a score even before they are fully completed. Thanks to the use of a skeleton-centric coordinate system, our recognition method has become view-invariant. We have successfully tested our action recognition method on both synthetic and real data. We have also compared our results with four state-of-the-art methods using three well known datasets for human action recognition. In particular, the comparisons have clearly shown the advantage of our method through better recognition rates.

Real-time and marker-free 3D motion capture for home entertainment oriented applications

We present an automated system for real-time marker-free motion capture from two calibrated webcams. For fast 3D shape and skin reconstructions, we extend Shape-From-Silhouette algorithms. The motion capture system is based on simple and fast heuristics to increase the efficiency. Multi-modal scheme using both shape and skin-parts analysis, temporal coherence, and human anthropometric constraints are adopted to increase the robustness. Thanks to fast algorithms, low-cost cameras and the fact that the system runs on a single computer, our system is perfectly suitable for home entertainment device. Results on real video sequences demonstrate our approach efficiency.

Coupled 2D and 3D Analysis for Moving Objects Detection with a Moving Camera

The detection of moving objects in the video stream of a moving camera is a complex task. Static objects appear moving in the video stream as moving objects. Thus, it is difficult to identify motions that belong to moving objects because they are hidden by those of static objects. To detect moving objects we propose a novel geometric constraint based on 2D and 3D information. A sparse reconstruction of the visible part of the scene is performed in order to detect motions in the 3D space where the scene perception is not deformed by the camera motion. A first labeling estimation is performed in the 3D space and then apparent motions in the video stream of the moving camera are used to validate the estimation. Labels are computed from confidence values which are updated at each frame according to the geometric constraint. Our method can detect several moving objects in complex scenes with high parallax.