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Author |
David Geronimo; Frederic Lerasle; Antonio Lopez |
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Title |
State-driven particle filter for multi-person tracking |
Type |
Conference Article |
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Year |
2012 |
Publication |
11th International Conference on Advanced Concepts for Intelligent Vision Systems |
Abbreviated Journal |
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Volume |
7517 |
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Pages |
467-478 |
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Keywords |
human tracking |
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Abstract |
Multi-person tracking can be exploited in applications such as driver assistance, surveillance, multimedia and human-robot interaction. With the help of human detectors, particle filters offer a robust method able to filter noisy detections and provide temporal coherence. However, some traditional problems such as occlusions with other targets or the scene, temporal drifting or even the lost targets detection are rarely considered, making the systems performance decrease. Some authors propose to overcome these problems using heuristics not explained
and formalized in the papers, for instance by defining exceptions to the model updating depending on tracks overlapping. In this paper we propose to formalize these events by the use of a state-graph, defining the current state of the track (e.g., potential , tracked, occluded or lost) and the transitions between states in an explicit way. This approach has the advantage of linking track actions such as the online underlying models updating, which gives flexibility to the system. It provides an explicit representation to adapt the multiple parallel trackers depending on the context, i.e., each track can make use of a specific filtering strategy, dynamic model, number of particles, etc. depending on its state. We implement this technique in a single-camera multi-person tracker and test
it in public video sequences. |
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Brno, Chzech Republic |
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Publisher |
Springer |
Place of Publication  |
Heidelberg |
Editor |
J. Blanc-Talon et al. |
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Language |
English |
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ACIVS |
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ADAS |
Approved |
yes |
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Call Number |
GLL2012; ADAS @ adas @ gll2012a |
Serial |
1990 |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin |
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Title |
Virtual Worlds and Active Learning for Human Detection |
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Conference Article |
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Year |
2011 |
Publication |
13th International Conference on Multimodal Interaction |
Abbreviated Journal |
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Pages |
393-400 |
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Keywords |
Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning |
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Abstract |
Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid. |
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Address |
Alicante, Spain |
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Publisher |
ACM DL |
Place of Publication |
New York, NY, USA, USA |
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Language |
English |
Summary Language |
English |
Original Title |
Virtual Worlds and Active Learning for Human Detection |
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978-1-4503-0641-6 |
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ICMI |
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ADAS |
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yes |
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Call Number |
ADAS @ adas @ VLP2011a |
Serial |
1683 |
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Author |
Yainuvis Socarras; Sebastian Ramos; David Vazquez; Antonio Lopez; Theo Gevers |
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Title |
Adapting Pedestrian Detection from Synthetic to Far Infrared Images |
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Conference Article |
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Year |
2013 |
Publication |
ICCV Workshop on Visual Domain Adaptation and Dataset Bias |
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Keywords |
Domain Adaptation; Far Infrared; Pedestrian Detection |
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Abstract |
We present different techniques to adapt a pedestrian classifier trained with synthetic images and the corresponding automatically generated annotations to operate with far infrared (FIR) images. The information contained in this kind of images allow us to develop a robust pedestrian detector invariant to extreme illumination changes. |
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Sydney; Australia; December 2013 |
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Place of Publication |
Sydney, Australy |
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English |
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ICCVW-VisDA |
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Notes |
ADAS; 600.054; 600.055; 600.057; 601.217;ISE |
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no |
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Call Number |
ADAS @ adas @ SRV2013 |
Serial |
2334 |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa |
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Title |
Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
3492 - 3495 |
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Keywords |
Pedestrian Detection; Domain Adaptation; Virtual worlds |
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Abstract |
Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1). |
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Address |
Tsukuba Science City, Japan |
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Publisher |
IEEE |
Place of Publication |
Tsukuba Science City, JAPAN |
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ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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Conference |
ICPR |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ VLP2012 |
Serial |
1981 |
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