Robustness and real-time performance of an insect inspired target tracking algorithm under natural conditions

• Many computer vision tasks require the implementation of robust and efficient target tracking algorithms. Furthermore, in robotic applications these algorithms must perform whilst on a moving platform (ego motion). Despite the increase in computational processing power, many engineering algorithms are still challenged by real-Time applications. In contrast, lightweight and low-power flying insects, such as dragonflies, can readily chase prey and mates within cluttered natural environments, deftly selecting their target amidst distractors (swarms). In our laboratory, we record from 'target-detecting' neurons in the dragonfly brain that underlie this pursuit behavior. We recently developed a closed-loop target detection and tracking algorithm based on key properties of these neurons. Here we test our insect-inspired tracking model in open-loop against a set of naturalistic sequences and compare its efficacy and efficiency with other state-of-The-Art engineering models. In terms of tracking robustness, our model performs similarly to many of these trackers, yet is at least 3 times more efficient in terms of processing speed.

Subject headings

NATURVETENSKAP  -- Biologi -- Zoologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Zoology (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

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