6,308 research outputs found
Generalized Regressive Motion: a Visual Cue to Collision
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?
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
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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