6,308 research outputs found

    Generalized Regressive Motion: a Visual Cue to Collision

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    Brains and sensory systems evolved to guide motion. Central to this task is controlling the approach to stationary obstacles and detecting moving organisms. Looming has been proposed as the main monocular visual cue for detecting the approach of other animals and avoiding collisions with stationary obstacles. Elegant neural mechanisms for looming detection have been found in the brain of insects and vertebrates. However, looming has not been analyzed in the context of collisions between two moving animals. We propose an alternative strategy, Generalized Regressive Motion (GRM), which is consistent with recently observed behavior in fruit flies. Geometric analysis proves that GRM is a reliable cue to collision among conspecifics, whereas agent-based modeling suggests that GRM is a better cue than looming as a means to detect approach, prevent collisions and maintain mobility

    Are object detection assessment criteria ready for maritime computer vision?

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    Maritime vessels equipped with visible and infrared cameras can complement other conventional sensors for object detection. However, application of computer vision techniques in maritime domain received attention only recently. The maritime environment offers its own unique requirements and challenges. Assessment of the quality of detections is a fundamental need in computer vision. However, the conventional assessment metrics suitable for usual object detection are deficient in the maritime setting. Thus, a large body of related work in computer vision appears inapplicable to the maritime setting at the first sight. We discuss the problem of defining assessment metrics suitable for maritime computer vision. We consider new bottom edge proximity metrics as assessment metrics for maritime computer vision. These metrics indicate that existing computer vision approaches are indeed promising for maritime computer vision and can play a foundational role in the emerging field of maritime computer vision

    Time Distance: A Novel Collision Prediction and Path Planning Method

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    Motion planning is an active field of research in robot navigation and autonomous driving. There are plenty of classical and heuristic motion planning methods applicable to mobile robots and ground vehicles. This paper is dedicated to introducing a novel method for collision prediction and path planning. The method is called Time Distance (TD), and its basis returns to the swept volume idea. However, there are considerable differences between the TD method and existing methods associated with the swept volume concept. In this method, time is obtained as a dependent variable in TD functions. TD functions are functions of location, velocity, and geometry of objects, determining the TD of objects with respect to any location. Known as a relative concept, TD is defined as the time interval that must be spent in order for an object to reach a certain location. It is firstly defined for the one-dimensional case and then generalized to 2D space. The collision prediction algorithm consists of obtaining the TD of different points of an object (the vehicle) with respect to all objects of the environment using an explicit function which is a function of TD functions. The path planning algorithm uses TD functions and two other functions called Z-Infinity and Route Function to create the collision-free path in a dynamic environment. Both the collision prediction and the path planning algorithms are evaluated in simulations. Comparisons indicate the capability of the method to generate length optimal paths as the most effective methods do
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