Zobrazeno 1 - 10
of 94
pro vyhledávání: '"Englund, Cristofer"'
In this paper, we present a new approach for facial anonymization in images and videos, abbreviated as FIVA. Our proposed method is able to maintain the same face anonymization consistently over frames with our suggested identity-tracking and guarant
Externí odkaz:
http://arxiv.org/abs/2309.04228
Autor:
Arvidsson, Moa, Sawirot, Sithichot, Englund, Cristofer, Alonso-Fernandez, Fernando, Torstensson, Martin, Duran, Boris
Millions of vehicles are transported every year, tightly parked in vessels or boats. To reduce the risks of associated safety issues like fires, knowing the location of vehicles is essential, since different vehicles may need different mitigation mea
Externí odkaz:
http://arxiv.org/abs/2307.10165
In this work, we present a new single-stage method for subject agnostic face swapping and identity transfer, named FaceDancer. We have two major contributions: Adaptive Feature Fusion Attention (AFFA) and Interpreted Feature Similarity Regularization
Externí odkaz:
http://arxiv.org/abs/2210.10473
Automatic detection of flying drones is a key issue where its presence, especially if unauthorized, can create risky situations or compromise security. Here, we design and evaluate a multi-sensor drone detection system. In conjunction with standard v
Externí odkaz:
http://arxiv.org/abs/2207.01927
Autor:
Henriksson, Jens, Berger, Christian, Borg, Markus, Tornberg, Lars, Sathyamoorthy, Sankar Raman, Englund, Cristofer
Several areas have been improved with Deep Learning during the past years. Implementing Deep Neural Networks (DNN) for non-safety related applications have shown remarkable achievements over the past years; however, for using DNNs in safety critical
Externí odkaz:
http://arxiv.org/abs/2204.12378
The use of small and remotely controlled unmanned aerial vehicles (UAVs), or drones, has increased in recent years. This goes in parallel with misuse episodes, with an evident threat to the safety of people or facilities. As a result, the detection o
Externí odkaz:
http://arxiv.org/abs/2111.01888
Autor:
Englund, Cristofer, Aksoy, Eren Erdal, Alonso-Fernandez, Fernando, Cooney, Martin Daniel, Pashami, Sepideh, Astrand, Bjorn
Smart Cities and Communities (SCC) constitute a new paradigm in urban development. SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities. Information and communication technology a
Externí odkaz:
http://arxiv.org/abs/2104.03150
Autor:
Henriksson, Jens, Berger, Christian, Borg, Markus, Tornberg, Lars, Sathyamoorthy, Sankar Raman, Englund, Cristofer
Several areas have been improved with Deep Learning during the past years. For non-safety related products adoption of AI and ML is not an issue, whereas in safety critical applications, robustness of such approaches is still an issue. A common chall
Externí odkaz:
http://arxiv.org/abs/2103.15580
Publikováno v:
Proc. Intl Conf on Pattern Recognition, ICPR, Milan, Italy, 10-15 January 2021
This paper explores the process of designing an automatic multi-sensor drone detection system. Besides the common video and audio sensors, the system also includes a thermal infrared camera, which is shown to be a feasible solution to the drone detec
Externí odkaz:
http://arxiv.org/abs/2007.07396
Autor:
Henriksson, Jens, Berger, Christian, Borg, Markus, Tornberg, Lars, Englund, Cristofer, Sathyamoorthy, Sankar Raman, Ursing, Stig
Deep Neural Networks (DNN) have improved the quality of several non-safety related products in the past years. However, before DNNs should be deployed to safety-critical applications, their robustness needs to be systematically analyzed. A common cha
Externí odkaz:
http://arxiv.org/abs/1903.01263