1,269 research outputs found
Self-organising zooms for decentralised redundancy management in visual sensor networks
When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras’ fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator’s preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator’s preferences
Visually Adversarial Attacks and Defenses in the Physical World: A Survey
Although Deep Neural Networks (DNNs) have been widely applied in various
real-world scenarios, they are vulnerable to adversarial examples. The current
adversarial attacks in computer vision can be divided into digital attacks and
physical attacks according to their different attack forms. Compared with
digital attacks, which generate perturbations in the digital pixels, physical
attacks are more practical in the real world. Owing to the serious security
problem caused by physically adversarial examples, many works have been
proposed to evaluate the physically adversarial robustness of DNNs in the past
years. In this paper, we summarize a survey versus the current physically
adversarial attacks and physically adversarial defenses in computer vision. To
establish a taxonomy, we organize the current physical attacks from attack
tasks, attack forms, and attack methods, respectively. Thus, readers can have a
systematic knowledge of this topic from different aspects. For the physical
defenses, we establish the taxonomy from pre-processing, in-processing, and
post-processing for the DNN models to achieve full coverage of the adversarial
defenses. Based on the above survey, we finally discuss the challenges of this
research field and further outlook on the future direction
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