Recently, several image gradient and edge based features have been introduced. In unison, they all discovered that object shape is a strong cue for recognition and tracking. Generally their basic feature extraction re...
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Recently, several image gradient and edge based features have been introduced. In unison, they all discovered that object shape is a strong cue for recognition and tracking. Generally their basic feature extraction relies on pixel-wise gradient or edge computation using discrete filter masks, while scale invariance is later achieved by higher level operations like accumulating histograms or abstracting edgels to line segments. In this paper we show a novel and fast way to compute region based gradient features which are scale invariant themselves. We developed specialized, quick learnable weak classifiers that are integrated into our adaptively boosted observation model for particle filter based tracking. With an ensemble of region based gradient features this observation model is able to reliably capture the shape of the tracked object. The observation model is adapted to new object and background appearances while tracking. Thus we developed advanced methods to decide when to update the model, or in other words, if the filter is on target or not. We evaluated our approach using the BoBoT1 as well as the PROST2 datasets.
Inspired by the success of deploying deep learning in the fields of computervision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In orde...
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Clustering of motion trajectories is highly relevant for human-robot interactions as it allows the anticipation of human motions, fast reaction to those, as well as the recognition of explicit gestures. Further, it al...
Clustering of motion trajectories is highly relevant for human-robot interactions as it allows the anticipation of human motions, fast reaction to those, as well as the recognition of explicit gestures. Further, it allows automated analysis of recorded motion data. Many clustering algorithms for trajectories build upon distance metrics that are based on pointwise Euclidean distances. However, our work indicates that focusing on salient characteristics is often sufficient. We present a novel distance measure for motion plans consisting of state and control trajectories that is based on a compressed representation built from their main features. This approach allows a flexible choice of feature classes relevant to the respective task. The distance measure is used in agglomerative hierarchical clustering. We compare our method with the widely used dynamic time warping algorithm on test sets of motion plans for the Furuta pendulum and the Manutec robot arm and on real-world data from a human motion dataset. The proposed method demonstrates slight advantages in clustering and strong advantages in runtime, especially for long trajectories.
This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects – in this case, falling corn grains. These type of cameras transmit information about the ...
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Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number...
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3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolu...
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Transformers have achieved remarkable success in several domains, ranging from natural language processing to computervision. Nevertheless, it has been recently shown that stacking self-attention layers — the distin...
ISBN:
(纸本)9781713871088
Transformers have achieved remarkable success in several domains, ranging from natural language processing to computervision. Nevertheless, it has been recently shown that stacking self-attention layers — the distinctive architectural component of Transformers — can result in rank collapse of the tokens' representations at initialization. The question of if and how rank collapse affects training is still largely unanswered, and its investigation is necessary for a more comprehensive understanding of this architecture. In this work, we shed new light on the causes and the effects of this phenomenon. First, we show that rank collapse of the tokens' representations hinders training by causing the gradients of the queries and keys to vanish at initialization. Furthermore, we provide a thorough description of the origin of rank collapse and discuss how to prevent it via an appropriate depth-dependent scaling of the residual branches. Finally, our analysis unveils that specific architectural hyperparameters affect the gradients of queries and values differently, leading to disproportionate gradient norms. This suggests an explanation for the widespread use of adaptive methods for Transformers' optimization.
Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number...
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This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects – in this case, falling corn grains. These type of cameras transmit information about the ...
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3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolu...
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