This paper details filtering subsystem for a tetravision based pedestrian detection system. The complete system is based on the use of both visible and far infrared cameras; in an initial phase it produces a list of areas of attention in the images which can contain pedestrians. This list is furtherly refined using symmetry-based assumptions. Then, this results is fed to a number of independent validators that evaluate the presence of human shapes inside the areas of attention.Histogram of oriented gradients and Support Vector Machines are used as a filter and demonstrated to be able to successfully classify up to 91% of pedestrians in the areas of attention.
Abstruct-This paper describes an approach for pedestrian detection in stem infrared images. The developed system has been implemented on an experimental vehicle equipped with two infrared camera and preliiariIy tested in diBerent situations. It is based on the localization and distance estimation of warm areas in the scene; the algorithm groups areas with similar position and considers only results with specific size and aspect ratio. A final validation process, based on the head shape's morphological and thermal characteristics, is used to build the list of potential pedestrian appearing in the scene. Neither temporal correlation, nor motion cues are used in this processing.
Abstract-This paper describes the research activities for the localization of human shapes using visual information carried on at the University of Parma, Italy, in the frame of a common project with the TACOM Department of U. S. Army.The paper proposes the application of a stereoscopic technique as a preprocessing for the localization of humans in generic unstructured environments. Each row of the left image is matched with the epipolar row of the right image. This creates a map of each object in the scene as well as the slope of the road. Preliminary results have proved to be promising.
Abstract-This article presents a validator stage for a pedestrian detection system based on the use of probabilistic models for the infrared domain. Four different models are employed in order to recognize the pose of the pedestrians; open, almost open, almost closed and fully closed legs are detected. In an attempt to overcome the drawbacks of template-matching in far infrared images, two different approaches are proposed. The algorithm has been tested on an experimental vehicle in different situations and a Receiver Operating Characteristic has been computed.
This paper describes a system for evaluating pedestrian detection algorithm results. The developed tool allows a human operator to annotate on a file all pedestrians in a previously acquired video sequence. A similar file is produced by the algorithm being tested using the same annotation engine. A matching rule has been established to validate the association between items of the two files. For each frame a statistical analyzer extracts the number of mis-detections, both positive and negative, and correct detections. Using these data, statistics about the algorithm behavior are computed with the aim of tuning parameters and pointing out recongnition weaknesses in particular situations.
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