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30 April 2009 Detection of moving targets from a moving ground platform
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Semi-autonomous operation of intelligent vehicles may require that such platforms maintain a basic situational awareness with respect to people, other vehicles and their intent. These vehicles should be able to operate safely among people and other vehicles, and be able to perceive threats and respond accordingly. A key requirement is the ability to detect people and vehicles from a moving platform. We have developed one such algorithm using video cameras mounted on the vehicle. Our person detection algorithms model the shape and appearance of the person instead of modeling the background. This algorithm uses histogram of oriented gradients (HOG), which model shape and appearance using image edge histograms. These HOG descriptors are computed on an exhaustive set of image windows, which are then classified as person/non-person using a support vector machine classifier. The image windows are computed using camera calibration, which provides approximate size of people with respect to their location in the imagery. The algorithm is flexible and has been trained for different domains such as urban, rural and wooded scenes. We have designed a sensor platform that can be mounted on a moving vehicle to collect video data of pedestrians. Using manually annotated ground-truth data we have evaluated the person detection algorithm in terms of true positive and false positive rates. This paper provides a detailed overview of the algorithm, describes the experiments conducted and reports on algorithmic performance.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas B. Sebastian, Christopher M. Wynnyk, Peter H. Tu, and Sabrina B. Barnes "Detection of moving targets from a moving ground platform", Proc. SPIE 7332, Unmanned Systems Technology XI, 733217 (30 April 2009);


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