A multi-viewpoint feature-based re-identification system driven by skeleton keypoints

Thanks 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

The GPS Assimilator: a Method for Upgrading Existing GPS User Equipment to Improve Accuracy, Robustness, and Resistance to Spoofing

Preprint of the 2010 ION GNSS Conference Portland, OR, September 21–24, 2010A conceptual method is presented for upgrading existing GPS user equipment, without requiring hardware or software modifications to the equipment, to improve the equipment’s position, velocity, and time (PVT) accuracy, to increase its PVT robustness in weak-signal or jammed environments, and to protect the equipment from counterfeit GPS signals (GPS spoofing). The method is embodied in a device called the GPS Assimilator that couples to the radio frequency (RF) input of an existing GPS receiver. The Assimilator extracts navigation and timing information from RF signals in its environment—including non-GNSS signals—and from direct baseband aiding provided, for example, by an inertial navigation system, a frequency reference, or the GPS user. The Assimilator optimally fuses the collective navigation and timing information to produce a PVT solution which, by virtue of the diverse navigation and timing sources on which it is based, is highly accurate and inherently robust to GPS signal obstruction and jamming. The Assimilator embeds the PVT solution in a synthesized set of GPS signals and injects these into the RF input of a target GPS receiver for which an accurate and robust PVT solution is desired. A prototype software-defined Assimilator device is presented with three example applications.Aerospace Engineerin

Advancing Strategy: How to Lead Change in Corporate Societal Engagement

Implementing a strategy may be even harder than developing it. This learning brief is intended for corporate foundation and CSR leaders who have completed an initial strategy refresh process and who seek effecitve practices and tools to advance this strategy. In our experience advising more than 100 multinational companie, effective leaders facilitate structured, data-informed decisions and enable important organizational improvements to achieve their strategic objectives. Specifically, advancing strategy in corporate societal engagement typically requires leading change in two major areas of the overall portfolio: designing a signative initiative and transforming local giving

Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model

A fundamental challenge in machine learning today is to build a model that can learn from few examples. Here, we describe a reservoir based spiking neural model for learning to recognize actions with a limited number of labeled videos. First, we propose a novel encoding, inspired by how microsaccades influence visual perception, to extract spike information from raw video data while preserving the temporal correlation across different frames. Using this encoding, we show that the reservoir generalizes its rich dynamical activity toward signature action/movements enabling it to learn from few training examples. We evaluate our approach on the UCF-101 dataset. Our experiments demonstrate that our proposed reservoir achieves 81.3%/87% Top-1/Top-5 accuracy, respectively, on the 101-class data while requiring just 8 video examples per class for training. Our results establish a new benchmark for action recognition from limited video examples for spiking neural models while yielding competetive accuracy with respect to state-of-the-art non-spiking neural models.Comment: 13 figures (includes supplementary information

Graphical model-based approaches to target tracking in sensor networks: an overview of some recent work and challenges

Sensor Networks have provided a technology base for distributed target tracking applications among others. Conventional centralized approaches to the problem lack scalability in such a scenario where a large number of sensors provide measurements simultaneously under a possibly non-collaborating environment. Therefore research efforts have focused on scalable, robust, and distributed algorithms for the inference tasks related to target tracking, i.e. localization, data association, and track maintenance. Graphical models provide a rigorous tool for development of such algorithms by modeling the information structure of a given task and providing distributed solutions through message passing algorithms. However, the limited communication capabilities and energy resources of sensor networks pose the additional difculty of considering the tradeoff between the communication cost and the accuracy of the result. Also the network structure and the information structure are different aspects of the problem and a mapping between the physical entities and the information structure is needed. In this paper we discuss available formalisms based on graphical models for target tracking in sensor networks with a focus on the aforementioned issues. We point out additional constraints that must be asserted in order to achieve further insight and more effective solutions