This paper examines the feasibility of a semantic-level driver activity analysis system. Several new considerations are made to construct the hierarchy of driver activity. Driver activity is represented and recognized...
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This paper examines the feasibility of a semantic-level driver activity analysis system. Several new considerations are made to construct the hierarchy of driver activity. Driver activity is represented and recognized at multiple levels: individual body-part pose/gesture at the low level, single body-part action at the middle level, and the driver interaction with the vehicle at the high level. Driving is represented in terms of the interactions among driver, vehicle, and surround, and driver activity is recognized by a rule-based decision tree. Our system works with a single color camera data, and it can be easily expanded to incorporate multimodal sensor data.
Driver assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively research and even put into commercial production. It is therefore important t...
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Driver assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively research and even put into commercial production. It is therefore important to take a critical look at key aspects of these systems, one of which being lane position tracking. In this paper we present an analysis of lane position tracking in the context of driver support systems and examine previous research in this area. Using this analysis we present a lane tracking system designed to work well under a variety of road and environmental conditions. We examine what types of metrics are important for evaluating lane position accuracy for specific overall system objectives. A detailed quantitative evaluation of the system is presented in this paper using a variety of metrics and test conditions.
The paper presents a system for automatic fire detection based on the use of autonomous aerial vehicles. Particularly, the application of a helicopter with infrared and visual cameras is described. The paper presents ...
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In this paper, we present a new area-based method for visual correspondence search that focuses on the dissimilarity computation. Local and area-based matching methods generally measure the similarity (or dissimilarit...
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ISBN:
(纸本)0769523722
In this paper, we present a new area-based method for visual correspondence search that focuses on the dissimilarity computation. Local and area-based matching methods generally measure the similarity (or dissimilarity) between the image pixels using local support windows. In this approach, an appropriate support window should be selected adaptively for each pixel to make the measure reliable and certain. Finding the optimal support window with an arbitrary shape and size is, however, very difficult and generally known as an NP-hard problem. For this reason, unlike the existing methods that try to find an optimal support window, we adjusted the support-weight of each pixel in a given support window. The adaptive support-weight of a pixel is computed based on the photometric and geometric relationship with the pixel under consideration. Dissimilarity is then computed using the raw matching costs and support-weights of both support windows, and the correspondence is finally selected by the WTA (winner-takes-all) method. The experimental results for the rectified real images show that the proposed method successfully produces piecewise smooth disparity maps while preserving sharp depth discontinuities accurately.
Automated video tracking is useful in a number of applications such as surveillance, multisensor networks, robotics and virtual reality. In this paper we investigate an approach to tracking based on fusing the output ...
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Automated video tracking is useful in a number of applications such as surveillance, multisensor networks, robotics and virtual reality. In this paper we investigate an approach to tracking based on fusing the output of a collection of video trackers, each attending to a different feature or cue on the target. We show both theoretically and experimentally that the method used to prune the growth of target hypotheses can have a great impact on the trackers performance, and indirectly, change the benefit of using linear score combination as opposed to a non-linear rank combination for fusion. We also show that the rank-score graph defined by Hsu and Taksa can be used to select a subset of features to fuse to reduce classification error.
In this paper we presented a novel, agile robot mechanism, which we call a rotopod, which combines aspects of wheeled and legged locomotion. A general description of how a tripedal rotopod can be made to step, rotatin...
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In this paper we presented a novel, agile robot mechanism, which we call a rotopod, which combines aspects of wheeled and legged locomotion. A general description of how a tripedal rotopod can be made to step, rotating the mechanism about one leg, and moving the center of the mechanism, is presented. The concept of a gait for this mechanism is defined, and is used to show how extremely agile the mechanism can be. Specific resistance is employed as a way to explore the relative efficiency of this mechanism versus a wheel. Finally, we describe our first prototype rotopod and report on experiments conducted to characterize stepping
An attractive approach to improve tracking performance for visual surveillance is to use information from multiple visual sensory cues such as position, color, shape, etc. Previous work in fusion for tracking has tend...
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An attractive approach to improve tracking performance for visual surveillance is to use information from multiple visual sensory cues such as position, color, shape, etc. Previous work in fusion for tracking has tended to focus on fusion by numerically combining the scores assigned by each cue. We argue that for video scenes with many targets in a crowded situation, the splitting and merging of regions associated with targets, and the subsequent dramatic changes in cue values and reliabilities, renders this form of fusion less effective. In this paper we present experimental results showing that use of cue rank information in fusion produces a significantly better tracking result in crowded scenes. We also present a formalization of this fusion problem as a step in understanding why this effect occurs and how to build a tracking system that exploits it.
In this paper we introduce a multilayer cylindrical histogram based feature space for representing 3D shape context of human body. Dynamics of gestures are analyzed using discrete hidden Markov models (DHMM) with quan...
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In this paper we introduce a multilayer cylindrical histogram based feature space for representing 3D shape context of human body. Dynamics of gestures are analyzed using discrete hidden Markov models (DHMM) with quantized feature vectors. Extensive experimental trials with multiple subjects and a range of gesture classes are presented. Gesture recognition accuracies of over 85% (for nine gestures, and 9 subjects) and over 95% (for seven gestures) support the basic feasibility and robustness of the approach.
This paper describes an approach for detecting objects in front of an automobile using wide field of view stereo with a pair of omni cameras. Several configurations are suggested for effective detection of vehicles an...
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This paper describes an approach for detecting objects in front of an automobile using wide field of view stereo with a pair of omni cameras. Several configurations are suggested for effective detection of vehicles and pedestrians. The omni cameras are calibrated using sets of parallel lines on a parking lot. The calibration is used to rectify the omni images. Stereo matching is performed on the rectified images to detect other vehicles and pedestrians. Experimental results show promise of detecting these objects on the road.
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