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Michael Arens

Bio: Michael Arens is an academic researcher from Fraunhofer Society. The author has contributed to research in topics: Video tracking & Object detection. The author has an hindex of 22, co-authored 193 publications receiving 2400 citations. Previous affiliations of Michael Arens include Karlsruhe Institute of Technology.


Papers
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Book ChapterDOI
Matej Kristan1, Ales Leonardis2, Jiří Matas3, Michael Felsberg4, Roman Pflugfelder5, Luka Cehovin1, Tomas Vojir3, Gustav Häger4, Alan Lukežič1, Gustavo Fernandez5, Abhinav Gupta6, Alfredo Petrosino7, Alireza Memarmoghadam8, Alvaro Garcia-Martin9, Andres Solis Montero10, Andrea Vedaldi11, Andreas Robinson4, Andy J. Ma12, Anton Varfolomieiev13, A. Aydin Alatan14, Aykut Erdem15, Bernard Ghanem16, Bin Liu, Bohyung Han17, Brais Martinez18, Chang-Ming Chang19, Changsheng Xu20, Chong Sun21, Daijin Kim17, Dapeng Chen22, Dawei Du20, Deepak Mishra23, Dit-Yan Yeung24, Erhan Gundogdu25, Erkut Erdem15, Fahad Shahbaz Khan4, Fatih Porikli26, Fatih Porikli27, Fei Zhao20, Filiz Bunyak28, Francesco Battistone7, Gao Zhu27, Giorgio Roffo29, Gorthi R. K. Sai Subrahmanyam23, Guilherme Sousa Bastos30, Guna Seetharaman31, Henry Medeiros32, Hongdong Li27, Honggang Qi20, Horst Bischof33, Horst Possegger33, Huchuan Lu21, Hyemin Lee17, Hyeonseob Nam34, Hyung Jin Chang35, Isabela Drummond30, Jack Valmadre11, Jae-chan Jeong36, Jaeil Cho36, Jae-Yeong Lee36, Jianke Zhu37, Jiayi Feng20, Jin Gao20, Jin-Young Choi, Jingjing Xiao2, Ji-Wan Kim36, Jiyeoup Jeong, João F. Henriques11, Jochen Lang10, Jongwon Choi, José M. Martínez9, Junliang Xing20, Junyu Gao20, Kannappan Palaniappan28, Karel Lebeda38, Ke Gao28, Krystian Mikolajczyk35, Lei Qin20, Lijun Wang21, Longyin Wen19, Luca Bertinetto11, Madan Kumar Rapuru23, Mahdieh Poostchi28, Mario Edoardo Maresca7, Martin Danelljan4, Matthias Mueller16, Mengdan Zhang20, Michael Arens, Michel Valstar18, Ming Tang20, Mooyeol Baek17, Muhammad Haris Khan18, Naiyan Wang24, Nana Fan39, Noor M. Al-Shakarji28, Ondrej Miksik11, Osman Akin15, Payman Moallem8, Pedro Senna30, Philip H. S. Torr11, Pong C. Yuen12, Qingming Huang20, Qingming Huang39, Rafael Martin-Nieto9, Rengarajan Pelapur28, Richard Bowden38, Robert Laganiere10, Rustam Stolkin2, Ryan Walsh32, Sebastian B. Krah, Shengkun Li19, Shengping Zhang39, Shizeng Yao28, Simon Hadfield38, Simone Melzi29, Siwei Lyu19, Siyi Li24, Stefan Becker, Stuart Golodetz11, Sumithra Kakanuru23, Sunglok Choi36, Tao Hu20, Thomas Mauthner33, Tianzhu Zhang20, Tony P. Pridmore18, Vincenzo Santopietro7, Weiming Hu20, Wenbo Li40, Wolfgang Hübner, Xiangyuan Lan12, Xiaomeng Wang18, Xin Li39, Yang Li37, Yiannis Demiris35, Yifan Wang21, Yuankai Qi39, Zejian Yuan22, Zexiong Cai12, Zhan Xu37, Zhenyu He39, Zhizhen Chi21 
08 Oct 2016
TL;DR: The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
Abstract: The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://votchallenge.net).

744 citations

Posted Content
TL;DR: This survey presents the mechanisms and properties of explaining systems for Deep Neural Networks for Computer Vision tasks, gives a comprehensive overview about the taxonomy of related studies and compares several survey papers that deal with explainability in general.
Abstract: Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized to be non-transparent and their predictions not traceable by humans. Furthermore, the models learn from artificial datasets, often with bias or contaminated discriminating content. Through their increased distribution, decision-making algorithms can contribute promoting prejudge and unfairness which is not easy to notice due to lack of transparency. Hence, scientists developed several so-called explanators or explainers which try to point out the connection between input and output to represent in a simplified way the inner structure of machine learning black boxes. In this survey we differ the mechanisms and properties of explaining systems for Deep Neural Networks for Computer Vision tasks. We give a comprehensive overview about taxonomy of related studies and compare several survey papers that deal with explainability in general. We work out the drawbacks and gaps and summarize further research ideas.

186 citations

Proceedings ArticleDOI
01 Aug 2016
TL;DR: It is shown that a low number of discriminatingly selected Integral Channel Features are sufficient to achieve state-of-the-art results on the MOT2015 and MOT2016 benchmark.
Abstract: Online multi-person tracking benefits from using an online learned appearance model to associate detections to tracks and further to close gaps in detections. Since Integral Channel Features (ICF) are popular for fast pedestrian detection, we propose an online appearance model that is using the same features without recalculation. The proposed method uses online Multiple-Instance Learning (MIL) to incrementally train an appearance model for each person discriminating against its surrounding. We show that a low number of discriminatingly selected Integral Channel Features are sufficient to achieve state-of-the-art results on the MOT2015 and MOT2016 benchmark.

105 citations

Book ChapterDOI
08 Oct 2016
TL;DR: The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance.
Abstract: The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.

84 citations

Journal ArticleDOI
TL;DR: A framework for on-the-fly comparison of current ALS data to given reference data of an urban area is presented and the proposed change detection method applies the Dempster–Shafer theory to identify conflicting evidence along the laser pulse propagation path.
Abstract: The use of helicopters as a sensor platform offers flexible fields of application due to adaptable flying speed at low flight levels. Modern helicopters are equipped with radar altimeters, inertial navigation systems (INS), forward-looking cameras and even laser scanners for automatic obstacle avoidance. If the 3D geometry of the terrain is already available, the analysis of airborne laser scanner (ALS) measurements may also be used for terrain-referenced navigation and change detection. In this paper, we present a framework for on-the-fly comparison of current ALS data to given reference data of an urban area. In contrast to classical difference methods, our approach extends the concept of occupancy grids known from robot mapping. However, it does not blur the measured information onto the grid cells. The proposed change detection method applies the Dempster–Shafer theory to identify conflicting evidence along the laser pulse propagation path. Additional attributes are considered to decide whether detected changes are of man-made origin or occurring due to seasonal effects. The concept of online change detection has been successfully validated in offline experiments with recorded ALS data streams. Results are shown for an urban test site at which multi-view ALS data were acquired at an interval of 1 year.

76 citations


Cited by
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Reference EntryDOI
15 Oct 2004

2,118 citations

Proceedings ArticleDOI
18 Jun 2018
TL;DR: The Siamese region proposal network (Siamese-RPN) is proposed which is end-to-end trained off-line with large-scale image pairs for visual object tracking and consists of SiAMESe subnetwork for feature extraction and region proposal subnetwork including the classification branch and regression branch.
Abstract: Visual object tracking has been a fundamental topic in recent years and many deep learning based trackers have achieved state-of-the-art performance on multiple benchmarks. However, most of these trackers can hardly get top performance with real-time speed. In this paper, we propose the Siamese region proposal network (Siamese-RPN) which is end-to-end trained off-line with large-scale image pairs. Specifically, it consists of Siamese subnetwork for feature extraction and region proposal subnetwork including the classification branch and regression branch. In the inference phase, the proposed framework is formulated as a local one-shot detection task. We can pre-compute the template branch of the Siamese subnetwork and formulate the correlation layers as trivial convolution layers to perform online tracking. Benefit from the proposal refinement, traditional multi-scale test and online fine-tuning can be discarded. The Siamese-RPN runs at 160 FPS while achieving leading performance in VOT2015, VOT2016 and VOT2017 real-time challenges.

2,016 citations

Proceedings Article
01 Jan 1999

2,010 citations

Proceedings ArticleDOI
21 Jul 2017
TL;DR: This work revisit the core DCF formulation and introduces a factorized convolution operator, which drastically reduces the number of parameters in the model, and a compact generative model of the training sample distribution that significantly reduces memory and time complexity, while providing better diversity of samples.
Abstract: In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing tracking performance, their characteristic speed and real-time capability have gradually faded. Further, the increasingly complex models, with massive number of trainable parameters, have introduced the risk of severe over-fitting. In this work, we tackle the key causes behind the problems of computational complexity and over-fitting, with the aim of simultaneously improving both speed and performance. We revisit the core DCF formulation and introduce: (i) a factorized convolution operator, which drastically reduces the number of parameters in the model, (ii) a compact generative model of the training sample distribution, that significantly reduces memory and time complexity, while providing better diversity of samples, (iii) a conservative model update strategy with improved robustness and reduced complexity. We perform comprehensive experiments on four benchmarks: VOT2016, UAV123, OTB-2015, and TempleColor. When using expensive deep features, our tracker provides a 20-fold speedup and achieves a 13.0% relative gain in Expected Average Overlap compared to the top ranked method [12] in the VOT2016 challenge. Moreover, our fast variant, using hand-crafted features, operates at 60 Hz on a single CPU, while obtaining 65.0% AUC on OTB-2015.

1,993 citations

01 Jan 1979
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

1,758 citations