This paper presents an analysis of color and infrared stereo approaches to pedestrian detection. We design a four camera experimental testbed consisting of two color and two infrared cameras that allows for synchronou...
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This paper presents an analysis of color and infrared stereo approaches to pedestrian detection. We design a four camera experimental testbed consisting of two color and two infrared cameras that allows for synchronous capture and direct frame-by-frame comparison of pedestrian detection approaches. We incorporate this four camera system in a test vehicle and conduct comparative experiments of stereo-based approaches to obstacle detection using color and infrared imagery. A detailed analysis of these experiments shows the robustness of both color and infrared stereo imagery to generate the dense stereo maps necessary for robust object detection and motivates investigation of color and infrared features that can be used to further classify detected obstacles into pedestrian regions. The complementary nature of color and infrared features gives rise to a discussion of a feature fusion techniques, including a cross-spectral stereo solution to pedestrian detection.
The control of the hand in primate species is characterized by a high dimensionality, due to the large number of joints in the fingers. In this study we present how its manipulation can be simplified without compromis...
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The control of the hand in primate species is characterized by a high dimensionality, due to the large number of joints in the fingers. In this study we present how its manipulation can be simplified without compromising its usage, through a constraint methodology that is inspired from recent neurobiological findings. We further develop a computational model, consisting of several brain areas related to hand control, using a co-evolutionary architecture. Due to its neurobiological basis the methodology gives rise to a number of emergent properties that have been shown to occur in primate species during reach-to-grasp tasks.
Recent brain imaging studies on primates revealed that a network of brain areas is activated both during observation and during execution of movements. The present work aims at modeling this group of areas, implementi...
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Recent brain imaging studies on primates revealed that a network of brain areas is activated both during observation and during execution of movements. The present work aims at modeling this group of areas, implementing a distributed computational system. The modeling process follows the agent-based coevolutionary framework that is very effective in terms of designing complex distributed systems addressing successfully the multi-modality of the interacting regions. The implemented model is successfully embedded in a simulated humanoid robot, replicating existing biological findings.
In this paper we present a framework for tracking non-rigid facial landmarks by combining various visual cues at multiple levels of detail. Using a probabilistic framework consisting of a hierarchy of particle filters...
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In this paper we introduce the NDS-Forest data structure, which can be used for the calculation and representation of Maximally Stable Extremal Regions in real-time video. In contrast to the standard MSER algorithm, t...
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ISBN:
(纸本)1904410146
In this paper we introduce the NDS-Forest data structure, which can be used for the calculation and representation of Maximally Stable Extremal Regions in real-time video. In contrast to the standard MSER algorithm, the NDS-Forest stores information about the extremal regions as they are formed, making it unnecessary to regrow the regions from seed pixels. Using the NDS-Forest structure, we describe a system that uses MSERs in an automobile for face registration, segmentation, and pose estimation of the driver.
This paper presents a method for registering multimodal imagery in short range surveillance situations when the differences in object depths preclude any global registration techniques. An analysis of multimodal regis...
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This paper proposes a concept of panoramic appearance map to perform reidentification of a people who leave the scene and reappear after some time. The map is a compact signature of appearance information of a person ...
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We propose a new approach to the hand pose estimation problem using only volume information. We describe a thermal and color image-based approach to generate silhouettes of the hand with which voxel images are produce...
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This paper presents an approach for the registration of multimodal imagery for pedestrian detection when the significant depth differences of objects in the scene precludes a global alignment assumption. Using maximiz...
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Driver assistance systems have both the potential to alert the driver to critical situations and distract or annoy the driver if the driver is already aware of the situation. As systems attempt to preemptively warn dr...
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Driver assistance systems have both the potential to alert the driver to critical situations and distract or annoy the driver if the driver is already aware of the situation. As systems attempt to preemptively warn drivers more and more in advance, this problem becomes exacerbated. We present a predictive braking assistance system that identifies not only the need for braking action, but also whether or not a braking action is being planned by the driver. Our system uses a Bayesian framework to determine the criticality of the situation by assessing (1) the probability that braking should be performed given observations of the vehicle and surround and (2) the probability that the driver intends to perform a braking action. We train and evaluate our system using over 22 hours of data collected from real driving scenarios with 28 different drivers
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