Integrated Computer-Aided Engineering

This paper introduces a novel spatial method for human action recognition that is discriminative without needing temporal information or action key poses. First, skeletal data is acquired with the Microsoft Kinect v2 sensor and undergoes a Pose Invariant Normalization (PIN) process. The PIN process translates, rotates and scales the various observed poses to eliminate body differences and positional differences between subjects. Second, the method uses a Body Related Occupancy Map (BROM), that describes in a 3D grid how the area around specific body parts is used, as a strong indicator of the particular action that is being performed. The BROM and its 2D projections are used as feature inputs for Random Forest classifiers. These classifiers are then combined in a hierarchic structure to boost the classification performance. The approach is tested on a self-captured database of 23 human actions for game-play. On this database a classification with an accuracy score of 91% is achieved for the hierarchic BROM (HiBROM) classification. On the public CAD60 dataset, the HiBROM classifier attains 87.2% accuracy which is comparable to other state-of-the-art methods.

This work proposes a system designed to estimate the ego-motion of a synchronized calibrated stereo camera in scenes containing a moderate number of moving objects. This is particularly useful in busy road scenes and populated urban areas. The key novelty of the proposed approach is that it estimates the motion of clusters of pixels between stereo frames, which allows it to explicitly reject clusters in motion. This is in contrast to current state-of-the-art algorithms, that tend to treat moving elements as outliers, which are removed using strategies such as RANSAC or M-estimators. Unfortunately treating moving pixels as outliers can give poor performance when the motion represents a significant portion of pixels. The proposed approach overcomes this, if the motion is due to many independently moving objects (such as people or cars). Our experiments show promising results in a variety of urban environments.

Shape reconstruction from images is one of the most widely adopted approaches to compute accurate 3D reconstructions of people or objects in a multi-camera environment. However, such algorithms are traditionally very sensitive to errors in the silhouettes due to imperfect foreground-background estimation or occluding objects appearing between the camera and the object of interest. We propose a novel algorithm that is still able to provide high quality reconstruction from incomplete silhouettes. At the core of the method is the partitioning of the reconstruction space in cells, i.e. regions with uniform camera and silhouette coverage properties. An iterative process is proposed which incrementally adds cells to the temporal reconstruction based on their potential to explain the observed silhouettes from different cameras. Experimental results are close to manually labelled approaches and outperform standard leave-M-out reconstruction techniques in terms of F1-score.

In this paper, we extend the discriminant correlation filter (DCF) based deep learning tracker to multi-object tracking. For each object, we use an individual tracker to estimate the position. Two different pre-trained networks are used as feature extractors, respectively. The response peak and oscillation are both considered to validate the tracking. When the object is lost, the discriminative appearance model achieved by DCF is considered as a part of the feature representation between the object and detection for data association. In order to validate our method, we analyze and test our approach on the MOT2D2015 and MOT17 benchmarks for multiple pedestrian tracking. The results show that our approach performs superiorly against several recent state-of-the-art online multi-object trackers.

A robust method is proposed for people tracking using range cameras: Density Map Tracking with Blob Splitting (DMT-BS). It was designed primarily for worker safety in industrial environments, but can be used in other applications as well. Multiple cameras can easily be added to resolve occlusions and to enlarge the observed area. The method could be used to track any moving object in an otherwise static environment as the detection does not rely on a specific human model. Its strength lies in its simplicity, making the behavior predictable and opening possibilities to be implemented on low-cost hardware. From the point cloud delivered by the depth sensor, a 2D density map is formed in floor coordinates followed by basic 2D-tracking. Robustness of this tracking is enhanced using a simple but effective blob splitting technique. Tests show that the camera position, depth noise, and extrinsic calibration errors have little influence on the tracker’s performance. The proposed method was tested on three depth tracking datasets, reaching significantly better MOTA (Multiple Object Tracking Accuracy) scores when compared to two state-of-the-art depth-based trackers.

The advances in microelectronics foster the Unmanned Aerial Vehicles (UAVs) to be used in many civil and academic applications that require higher levels of autonomy and flight stabilization. The main objective in this paper is to provide UAVs with a robust navigation system; in order to allow the UAVs to perform complex tasks autonomously and in real-time. The proposed algorithms deal with solving the navigation problem for outdoor and indoor environments, mainly based on visual information that is captured by monocular cameras. This paper covers the topics of Pose Estimation, Navigation Guidance, and Visual Servoing. All the proposed algorithms have been verified with real flights in both indoor and outdoor environments, taking into consideration the visual conditions; such as illumination and textures. The obtained results have been validated against other systems; such as VICON motion capture system, DGPS in the case of pose estimate algorithm. In addition, the proposed algorithms have been compared with several previous works in the state of the art, and the results prove the improvement in the accuracy and the robustness of the proposed algorithms.