A multi-aperture optical flow estimation method for an artificial compound eye

Wu, Sidong; Zhang, Gexiang; Neri, Ferrante; Zhu, Ming; Jiang, Tao; Kuhnert, Klaus-Dieter

doi:10.3233/ica-180593

Cited by 12 publications

(11 citation statements)

References 59 publications

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“…Our aim is to refine the work presented here and better address the constraints of fall detection systems. Going forward, we plan to use deep learning (DL) methods due to their performances as mentioned in recent works [19,20] to more accurately recognize human posture. We will start by 4 postures (standing up, sitting, lying on the ground and lying on a bed or sofa) in the context of fall detection and also fall prevention by activity analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Several features based on temperature thresholds are proposed to be evaluated by a Support Vector Machine (SVM) in the classification. However, in [19,20] authors have proposed another type of thermal sensor but relatively expensive. In [21], a very economical thermal imaging based input modality is proposed to detect falls using the optical flow of human movements tested on public datasets.…”

Section: Related Workmentioning

confidence: 99%

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Depth and thermal information fusion for head tracking using particle filter in a fall detection context

Halima

Laferté

Cormier³

et al. 2020

ICA

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The security of elderly people living alone is a major issue. A system that detects anomalies can be useful for both individual and retirement homes. In this paper, we present an adaptive human tracking method built on particle filter, using depth and thermal information based on the velocity and the position of the head. The main contribution of this paper is the fusion of information to improve tracking. For each frame, there is a new combination of coefficients for each particle based on an adaptive weighting. Results show that the tracking method can deal with the cases of fast motion (fall), partial occultation and scale variation. To assess the impact of fusion on the tracking process, the robustness and accuracy of the method are tested on a variety of challenging scenarios with or without depth-thermal fusion.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Depth and thermal information fusion for head tracking using particle filter in a fall detection context

Halima

Laferté

Cormier³

et al. 2020

ICA

View full text Add to dashboard Cite

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“…Due to industrial demand, the pattern analysis by DNNs has been introduced for contentbased image retrieval [10], noisy image recognition [11], 3D medical image super-resolution [12], video surveillance [13], foreground detection [14], multi-object tracking [15], explosive device detection [16], pupil detection [17], online data streaming [18], airport baggage handling [19], and many other objectives. Computational photography [20] and purpose-specific machine learning [21] have been also employed to solve industrial problems.…”

Section: Pattern Analysis For Fabric Imagesmentioning

confidence: 99%

Image-based textile decoding

Chen

Toyoura

Terada

et al. 2021

ICA

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A textile fabric consists of countless parallel vertical yarns (warps) and horizontal yarns (wefts). While common looms can weave repetitive patterns, Jacquard looms can weave the patterns without repetition restrictions. A pattern in which the warps and wefts cross on a grid is defined in a binary matrix. The binary matrix can define which warp and weft is on top at each grid point of the Jacquard fabric. The process can be regarded as encoding from pattern to textile. In this work, we propose a decoding method that generates a binary pattern from a textile fabric that has been already woven. We could not use a deep neural network to learn the process based solely on the training set of patterns and observed fabric images. The crossing points in the observed image were not completely located on the grid points, so it was difficult to take a direct correspondence between the fabric images and the pattern represented by the matrix in the framework of deep learning. Therefore, we propose a method that can apply the framework of deep learning viau the intermediate representation of patterns and images. We show how to convert a pattern into an intermediate representation and how to reconvert the output into a pattern and confirm its effectiveness. In this experiment, we confirmed that 93% of correct pattern was obtained by decoding the pattern from the actual fabric images and weaving them again.

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“…Among the many variants of P Systems, Numerical P Systems (NPS) [49] and Enzymatic Numerical P Systems (ENPS) [61] are amongst the most successful due to their high performance in robots' control applications [7,8,41,63,13], especially for modular and complex tasks of autonomous mobile robots (AMR), see also [12,42,65,47,3]. The success of NPS and ENPS in robotics is due to the inherent parallel and distributed nature of these P systems along with their powerful numerical computation power [43].…”

Section: Introductionmentioning

confidence: 99%

Multi-behaviors coordination controller design with enzymatic numerical P systems for robots

Wang

Zhang

Gou

et al. 2021

ICA

Self Cite

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Membrane computing models are parallel and distributed natural computing models. These models are often referred to as P systems. This paper proposes a novel multi-behaviors co-ordination controller model using enzymatic numerical P systems for autonomous mobile robots navigation in unknown environments. An environment classifier is constructed to identify different environment patterns in the maze-like environment and the multi-behavior co-ordination controller is constructed to coordinate the behaviors of the robots in different environments. Eleven sensory prototypes of local environments are presented to design the environment classifier, which needs to memorize only rough information, for solving the problems of poor obstacle clearance and sensor noise. A switching control strategy and multi-behaviors coordinator are developed without detailed environmental knowledge and heavy computation burden, for avoiding the local minimum traps or oscillation problems and adapt to the unknown environments. Also, a serial behaviors control law is constructed on the basis of Lyapunov stability theory aiming at the specialized environment, for realizing stable navigation and avoiding actuator saturation. Moreover, both environment classifier and multi-behavior coordination controller are amenable to the addition of new environment models or new behaviors due to the modularity of the hierarchical architecture of P systems. The simulation of wheeled mobile robots shows the effectiveness of this approach.

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A multi-aperture optical flow estimation method for an artificial compound eye

Cited by 12 publications

References 59 publications

Depth and thermal information fusion for head tracking using particle filter in a fall detection context

Depth and thermal information fusion for head tracking using particle filter in a fall detection context

Image-based textile decoding

Multi-behaviors coordination controller design with enzymatic numerical P systems for robots

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