A Rotational Motion Perception Neural Network Based on Asymmetric Spatiotemporal Visual Information Processing

Hu, Bin; Yue, Shigang; Zhang, Zhuhong

doi:10.1109/tnnls.2016.2592969

Cited by 25 publications

(12 citation statements)

References 66 publications

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“…This approach has been also built on the embedded system in a miniaturised mobile robot [65]. Furthermore, a visual neural network that sense rotational or spiral motion patterns integrated mechanisms of the locust DSNs and the fly EMDs in a computational structure [99,100]: this model can well recognise both clockwise and anticlockwise rotations of an object in a simple background.…”

Section: Multiple Neural Systems Integrationmentioning

confidence: 99%

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Wang

et al. 2019

Artificial Life

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Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging and so forth. A good number of motion detectors have been identified in the insects' visual pathways.Computational modelling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research of insects' visual systems in the literature. These motion perception models or neural networks comprise the looming sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation sensitive neural systems of direction selective neurons (DSNs) in fruit flies, bees and locusts, as well as the small target motion detectors (STMDs) in dragonflies and hover flies. We also review the applications of these models to robots and vehicles. Through these modelling studies, we summarise the methodologies that generate different direction and size selectivity in motion perception. At last, we discuss about multiple systems integration and hardware realisation of these bio-inspired motion perception models.

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Section: Multiple Neural Systems Integrationmentioning

confidence: 99%

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Wang

et al. 2019

Artificial Life

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“…The characteristic array of directional sensitive tangential cells found in the lobula plate of many flies are not found in locusts [9]. Therefore unlike those directional selective neurons [9], [32], [34], [41], [51]- [53], we model LGMD2 to only react selectively to looming stimuli with changes in extent, rather than the four cardinal directional movements [7], [8]. The above experiments with X-Y planes stimuli on different directions (Fig.…”

Section: E Discussionmentioning

confidence: 99%

Collision Selective Visual Neural Network Inspired by LGMD2 Neurons in Juvenile Locusts

Fu,

Hu,

Yue

2018

Preprint

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For autonomous robots in dynamic environments mixed with human, it is vital to detect impending collision quickly and robustly. The biological visual systems evolved over millions of years may provide us efficient solutions for collision detection in complex environments. In the cockpit of locusts, two Lobula Giant Movement Detectors, i.e. LGMD1 and LGMD2, have been identified which respond to looming objects rigorously with high firing rates. Compared to LGMD1, LGMD2 matures early in the juvenile locusts with specific selectivity to dark moving objects against bright background in depth while not responding to light objects embedded in dark background -a similar situation which ground vehicles and robots are facing with. However, little work has been done on modeling LGMD2, let alone its potential in robotics and other vision-based applications. In this article, we propose a novel way of modeling LGMD2 neuron, with biased ON and OFF pathways splitting visual streams into parallel channels encoding brightness increments and decrements separately to fulfill its selectivity. Moreover, we apply a biophysical mechanism of spike frequency adaptation to shape the looming selectivity in such a collision-detecting neuron model. The proposed visual neural network has been tested with systematic experiments, challenged against synthetic and real physical stimuli, as well as image streams from the sensor of a miniature robot. The results demonstrated this framework is able to detect looming dark objects embedded in bright backgrounds selectively, which make it ideal for ground mobile platforms. The robotic experiments also showed its robustness in collision detection -it performed well for near range navigation in an arena with many obstacles. Its enhanced collision selectivity to dark approaching objects versus receding and translating ones has also been verified via systematic experiments.

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“…In the contrast pathway, the directional contrast of each pixel along different spatial directions is calculated by convolving the ommatidium output with directional derivative operators (see Equation (17)). Since the 2-D spatial convolution needs O(k 2 mn) cost for each spatial direction, the entire computational complexity of the contrast pathway is O(k 2 dmn).…”

Section: Appendixmentioning

confidence: 99%

A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds

Wang

Peng

Zheng

et al. 2020

IEEE Trans. Neural Netw. Learning Syst.

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Monitoring small objects against cluttered moving backgrounds is a huge challenge to future robotic vision systems. As a source of inspiration, insects are quite apt at searching for mates and tracking prey -which always appear as small dim speckles in the visual field. The exquisite sensitivity of insects for small target motion, as revealed recently, is coming from a class of specific neurons called small target motion detectors (STMDs). Although a few STMD-based models have been proposed, these existing models only use motion information for small target detection and cannot discriminate small targets from smalltarget-like background features (named as fake features). To address this problem, this paper proposes a novel visual system model (STMD+) for small target motion detection, which is composed of four subsystems -ommatidia, motion pathway, contrast pathway and mushroom body. Compared to existing STMD-based models, the additional contrast pathway extracts directional contrast from luminance signals to eliminate false positive background motion. The directional contrast and the extracted motion information by the motion pathway are integrated in the mushroom body for small target discrimination. Extensive experiments showed the significant and consistent improvements of the proposed visual system model over existing STMD-based models against fake features.

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A Rotational Motion Perception Neural Network Based on Asymmetric Spatiotemporal Visual Information Processing

Cited by 25 publications

References 66 publications

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Collision Selective Visual Neural Network Inspired by LGMD2 Neurons in Juvenile Locusts

A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds

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