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 (egomotion). 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.