1,721,167 research outputs found
A multi-viewpoint feature-based re-identification system driven by skeleton keypoints
Full text linkThanks to the increasing popularity of 3D sensors, robotic vision has experienced huge improvements in a wide range of applications and systems in the last years. Besides the many benefits, this migration caused some incompatibilities with those systems that cannot be based on range sensors, like intelligent video surveillance systems, since the two kinds of sensor data lead to different representations of people and objects. This work goes in the direction of bridging the gap, and presents a novel re-identification system that takes advantage of multiple video flows in order to enhance the performance of a skeletal tracking algorithm, which is in turn exploited for driving the re-identification. A new, geometry-based method for joining together the detections provided by the skeletal tracker from multiple video flows is introduced, which is capable of dealing with many people in the scene, coping with the errors introduced in each view by the skeletal tracker. Such method has a high degree of generality, and can be applied to any kind of body pose estimation algorithm. The system was tested on a public dataset for video surveillance applications, demonstrating the improvements achieved by the multi-viewpoint approach in the accuracy of both body pose estimation and re-identification. The proposed approach was also compared with a skeletal tracking system working on 3D data: the comparison assessed the good performance level of the multi-viewpoint approach. This means that the lack of the rich information provided by 3D sensors can be compensated by the availability of more than one viewpoint
Fast Calibration Method for Active Cameras
No full textIn this paper a model for active cameras that considers complex camera dynamics and lens distortion is presented. This model is particularly suited for real-time applications, thanks to the low computational load required when the active camera is moved. In addition, a simple technique for interpolating calibration parameters is described, resulting in very accurate calibration over the full range of focal lengths. The proposed system can be employed to enhance the patrolling activity performed by a network of active cameras that supervise large areas. Experiments are also presented, showing the improvement provided over traditional pin-hole camera models
How could a subcellular image, or a painting by Van Gogh, be similar to a great white shark or to a pizza?
No full textSkeleton-Based Action and Gesture Recognition for Human-Robot Collaboration
Full text linkHuman action recognition plays a major role in enabling an effective and safe collaboration between humans and robots. Considering for example a collaborative assembly task, the human worker can use gestures to communicate with the robot while the robot can exploit the recognized actions to anticipate the next steps in the assembly process, improving safety and the overall productivity. In this work, we propose a novel framework for human action recognition based on 3D pose estimation and ensemble techniques. In such framework, we first estimate the 3D coordinates of the human hands and body joints by means of OpenPose and RGB-D data. The estimated joints are then fed to a set of graph convolutional networks derived from Shift-GCN, one network for each set of joints (i.e., body, left hand and right hand). Finally, using an ensemble approach we average the output scores of all the networks to predict the final human action. The proposed framework was evaluated on a dedicated dataset, named IAS-Lab Collaborative HAR dataset, which includes both actions and gestures commonly used in human-robot collaboration tasks. The experimental results demonstrated how the ensemble of the different action recognition models helps improving the accuracy and the robustness of the overall system
Comparison of Multi-scale Approaches for Extracting Image Descriptors from the Co-occurrence Matrix
No full textOne of the first methods for analyzing the texture of an image was proposed in 1979 by Haralick, who introduced the co-occurrence matrix for calculating a set of image statistics. In this paper we focus on novel texture descriptors extracted from the co-occurrence matrix. It is well known that scale is important information in texture analysis, since the same texture can be perceived as different patterns at distinct scales. In this work we present, compare and combine different strategies for extending the texture descriptors extracted from the co-occurrence matrix at multiple scales. The texture descriptors are used to train a support vector machine and some different fusion techniques are compared. Our results are validated using seven image classification problems (mainly medical image classification problems). Our results shown that we improve the performance of the standard approaches. The code for the approaches tested in this paper is available at: http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview=
Towards Cooperative People Re-Identification between 3D Sensors and 2D Camera Networks
No full textThanks to the increasing popularity of 3D sensors, robotics vision has experienced huge improvements in a wide range of applications and systems in the last years. Besides the many benets, this migration caused some incompatibilities with those systems that cannot be based on range sensors, like intelligent video surveillance systems, since the two kinds of sensor data lead to dierent representations of people and objects. This work goes in the direction of bridging the gap, and presents a novel re-identication system that takes advantage of multiple video flows in order to enhance the performance of a skeletal tracking algorithm, which is in turn exploited for driving the re-identication. A new, geometry-based method for joining together the detections provided by the skeletal tracker from multiple video flows is introduced: it is capable of dealing with many people in the scene, and of rejecting the errors introduced in each view by the skeletal tracker. Such method has a high degree of generality, and it can be applied to any body pose estimation algorithm. The system was tested on a public dataset for video surveillance applications, demonstrating the improvements achieved by the multi-viewpoint approach in the accuracy of both the body pose estimation and the re-identication. This means that the lack of the rich information provided by 3D sensors can be compensated by the availability of more than one viewpoint
Automatic Color Inspection for Colored Wires in Electric Cables
Full text linkIn this paper, an automatic optical inspection system for checking the sequence of colored wires in electric cable is presented. The system is able to inspect cables with flat connectors differing in the type and number of wires. This variability is managed in an automatic way by means of a self-learning subsystem and does not require manual input from the operator or loading new data to the machine. The system is coupled to a connector crimping machine and once the model of a correct cable is learned, it can automatically inspect each cable assembled by the machine. The main contributions of this paper are: (i) the self-learning system; (ii) a robust segmentation algorithm for extracting wires from images even if they are strongly bent and partially overlapped; (iii) a color recognition algorithm able to cope with highlights and different finishing of the wire insulation. We report the system evaluation over a period of several months during the actual production of large batches of different cables; tests demonstrated a high level of accuracy and the absence of false negatives, which is a key point in order to guarantee defect-free productions
Thermobot: towards Semi-Autonomous, Thermographic Detection of Cracks
No full textThe detection of cracks in parts of complex geometry requires a cumbersome process based on “magnetic particle inspection”. This includes the application and removal of liquids and is difficult to automate. In this paper a semi-autonomous system for crack detection is proposed that uses a robot to move a part in front of a thermographic image acquisition system. At the heart of the inspection system is a laser combined with a thermocamera that will provide image information to enable the robust detection of cracks on or near the surface. The analysis of the heat flow will reveal any inhomogeneities such as cracks in
the part. This is combined with automatic path planning for the robot to enable the inspection of complex parts. The system concept is presented and details about the various system components are explained
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