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.
|Title of host publication||2015 IEEE Symposium Series on Computational Intelligence|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Publication status||Published - 2015|
|Event||IEEE Symposium Series on Computational Intelligence, SSCI 2015 - Cape Town, South Africa|
Duration: 8 Dec 2015 → 10 Dec 2015
|Conference||IEEE Symposium Series on Computational Intelligence, SSCI 2015|
|Period||8/12/15 → 10/12/15|