Motion detection and object tracking with discrete leaky integrate-and-fire neurons

Risinger, Lon; Kaikhah, Khosrow

doi:10.1007/s10489-007-0092-9

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…In order to perform reliable matching between edges, some methods use alternative descriptors/features of object shape instead of considering all edge points [8,16,17]. Various static or dynamic curve representations including Fourier descriptor [18], Hough transformations [19,20], turning functions [21], or curve bending functions [22] are used for matching.…”

Section: Related Workmentioning

confidence: 99%

A flexible edge matching technique for object detection in dynamic environment

2011

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Considering the robustness, stability and reduced volume of data, researchers have focused on using edge information in various video processing applications including moving object detection, tracking and target recognition. Though the edge information is more robust compared to intensity, it also exhibits variations in different frames due to illumination change and noise. In addition to this, the amount of variation varies from edge to edge. Thus, without making use of this variability information, it is difficult to obtain an optimal performance during edge matching. However, traditional edge pixel-based methods do not keep structural information of edges and thus they are not suitable to extract and hold this variability information. To achieve this, we represent edges as segments that make use of the structural and relational information of edges to allow extraction of this variability information. During edge matching, existing algorithms do not handle the size, positional and rotational variations to deal with edges of arbitrary shapes. In this paper, we propose a knowledge-based flexible edge matching algorithm where knowledge is obtained from the statistics on the environmental dynamics, and flexibility is to deal with the arbitrary shape and the geometric variations of edges by making use of this knowledge. In this paper, we detailed the effectiveness of the proposed matching algorithm in moving object detection and also indicated its suitability in other applications like target detection and tracking.

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Section: Related Workmentioning

confidence: 99%

A flexible edge matching technique for object detection in dynamic environment

2011

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“…Various Spiking Neuron Networks models have been proposed for motion processing, such as [26], the authors have established a large-scale spiking model of the visual area MT capable of exhibiting both component and pattern motion selectivity. In [27], the authors implemented a biologically inspired visual system for motion detection and pursuit with a Discrete Leaky Integrate-and-Fire (DLIF) neuron model. In [28], the authors proposed bioinspired motion features for action recognition and modeled different MT cells.…”

Section: Introductionmentioning

confidence: 99%

The Hodgkin–Huxley neuron model for motion detection in image sequences

Yedjour

Meftah

Yedjour

et al. 2021

Neural Comput & Applic

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In this paper, we consider a biologically-inspired spiking neural network model for motion detection. The proposed model simulates the neurons' behavior in the cortical area MT to detect different kinds of motion in image sequences. We choose the conductance-based neuron model of the Hodgkin-Huxley to define MT cell responses. Based on the center-surround antagonism of MT receptive fields, we model the area MT by its great proportion of cells with directional selective responses. The network's spiking output corresponds to an MT neuron population's firing rates and enables to extract motion boundaries. We conduct several experiments on real image sequences. The experimental results show the proposed network's ability to segregate multiple moving objects from an image sequence and reproduce the MT cells' responses. We perform a quantitative evaluation on the YouTube Motion Boundaries (YMB) dataset, and we compare the result to state-ofthe-art methods for boundary detection in videos: boundary flow estimation (BF) and temporal boundary difference (BD). The proposed network model provides the best results on YMB compared to BF and BD methods.

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“…Motion estimation plays important roles in a number of applications such as automobile navigation, video coding, surveillance cameras and so forth. The measurement of the motion vector is a fundamental problem in image processing and computer vision, which has been faced using several approaches [1][2][3][4]. The goal is to compute an approximation to the 2-D motion field -a projection of the 3-D velocities of surface points onto the imaging surface.…”

Section: Introductionmentioning

confidence: 99%

“…Although such approaches have been algorithmically considered as the fastest, they are not able eventually to match the dynamic motioncontent, delivering false motion vectors (image distortions). (2) Reducing the search points: in this method, the algorithm chooses as search points exclusively those locations which iteratively minimize the errorfunction (SAD values). This category includes: the Adaptive Rood Pattern Search (ARPS) [17], the Fast Block Matching Using Prediction (FBMAUPR) [18], the Block-based Gradient Descent Search (BBGD) [19] and the Neighbourhood Elimination algorithm (NE) [20].…”

Section: Introductionmentioning

confidence: 99%

Block-matching algorithm based on harmony search optimization for motion estimation

Cuevas

2012

Appl Intell

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Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing a certain matching metric that is produced for the current frame over a determined search window from the previous frame. Unfortunately, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. The simplest available BM method is the Full Search Algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of all the elements of the search space. Recently, several fast BM algorithms have been proposed to reduce the search positions by calculating only a fixed subset of motion vectors despite lowering its accuracy. On the other hand, the Harmony Search (HS) algorithm is a population-based optimization method that is inspired by the music improvisation process in which a musician searches for harmony and continues to polish the pitches to obtain a better harmony. In this paper, a new BM algorithm that combines HS with a fitness approximation model is proposed. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate. In order to save computational time, the approach incorporates a fitness calculation strategy to decide which motion vectors can be only estimated or actually evaluated. Guided by the values of such fitness calculation strategy, the set of motion vectors is evolved through HS operators until the best possible motion vector is identified. The proposed method has been compared to other BM algorithms in terms of velocity and coding quality. Experimental results demonstrate that the proposed algorithm exhibits the best balance between coding efficiency and computational complexity.

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Motion detection and object tracking with discrete leaky integrate-and-fire neurons

Cited by 10 publications

References 13 publications

A flexible edge matching technique for object detection in dynamic environment

A flexible edge matching technique for object detection in dynamic environment

The Hodgkin–Huxley neuron model for motion detection in image sequences

Block-matching algorithm based on harmony search optimization for motion estimation

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