Multi-dimensional visual tracking using scatter search particle filter

Pantrigo, Juan José; Sánchez, Ángel Mediavilla; Montemayor, Antonio S.; Duarte, Abraham

doi:10.1016/j.patrec.2007.12.012

Cited by 23 publications

(20 citation statements)

References 12 publications

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“…In particular, particle filters have emerged as a very powerful tool to perform tracking in a wide variety of applications [3] [4]. Many of these works assume a given initial detection and perform tracking using likelihood models based on appearance templates.…”

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confidence: 99%

Vehicle detection and tracking using homography-based plane rectification and particle filtering

Arróspide

Salgado

Nieto

2010

2010 IEEE Intelligent Vehicles Symposium

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Abstract-This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results.I. INTRODUCTION Nowadays advanced driver assistance systems receive increasing interest both commercially and from the scientific community. In particular, much work has been devoted to the research on techniques for the detection of vehicles in traffic scenarios based on monocular computer vision, due to its low cost and good performance. Aside from traditional knowledge or feature-based approaches, which are dependent on the specific conditions (e.g., weather, illumination), use of motion information is in the basis of many recent works in the field, as this is inherent to the vehicles regardless of the conditions.Usually movement on a planar surface (i.e., the road) is assumed. Nonetheless, environments using non-stationary camera settings, such as the considered traffic environment, feature motion both of the own vehicle (ego-motion) and of the surrounding vehicles. Many methods have been presented that aim at computing ego-motion in order to decouple these motions [1][2]. However, reliable and efficient ego motion estimation in still and open issue.As for vehicle tracking, statistical approaches are adopted in many recent works. In particular, particle filters have emerged as a very powerful tool to perform tracking in a wide variety of applications [3] [4]. Many of these works assume a given initial detection and perform tracking using likelihood models based on appearance templates. However such detection-by-tracking approaches carry the danger of drifting away from the correct targets [5]. Additionally, tracking is often limited to a constant number of objects.

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confidence: 99%

Vehicle detection and tracking using homography-based plane rectification and particle filtering

Arróspide

Salgado

Nieto

2010

2010 IEEE Intelligent Vehicles Symposium

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“…In [10], Krzeszowski et al use a Particle Filter embedding PSO to shift the particles toward more promising configurations of the human model. Similar approaches are presented in [18] and in [24], where Scatter Search is used instead of PSO. In [25], the authors use PCA to reduce the dimensionality of the problem, which is tackled using an ad-hoc Hierarchical Annealed Genetic Algorithm.…”

Section: Introductionmentioning

confidence: 99%

Differential evolution based human body pose estimation from point clouds

Ugolotti

Cagnoni

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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This paper describes a method to estimate the body pose of a human from the point cloud obtained from a depth sensor. It uses Differential Evolution to find the best match between a candidate pose, represented by an instance of a 42-parameter articulated model of a human, and the point cloud. The results, compared to other four state-of-the art methods on a publicly available dataset, show that the method has good ability to estimate the pose of a person and to track him in video sequences.The entire method, from Differential Evolution to fitness computation, is run on nVIDIA GPUs. Thanks to its massively parallel implementation in CUDA-C, it produces pose estimates in real time.

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“…1 There are many classical tracking frameworks and algorithms. [1][2][3][4][5][6][7][8][9][10][11] One of the most widely used tracking frameworks is Kalman filtering (KF). 4,5,12 Kalman filter is an optimal recursive estimator of a dynamic system state.…”

Section: Introductionmentioning

confidence: 99%

A self-adaptive edge matching method based on mean shift and its application in video tracking

Liu

Zhou

2013

The Imaging Science Journal

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A self-adaptive edge matching method based on mean shift adjustment is proposed in this paper. Such method uses the local mode seeking character of mean shift to adjust the edge information of each model to a stable state before matching, which can effectively avoid the deviation problem of traditional method and raise the successful matching rate. Furthermore, the interfering vector with a self-adaptive coefficient is proposed to optimise the matching performance in complex background. Compared with a pre-set constant coefficient, the self-adapted coefficient has a better perception of background edge complexity so as to control the initial adjusting position more rationally, and thus increases the robustness and accuracy of matching. This matching method is applied in an improved particle filtering tracking framework, and experimental results prove the validity and rationality of the theoretical analysis, and show that the proposed matching method performs a robust and efficient tracking.

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Multi-dimensional visual tracking using scatter search particle filter

Cited by 23 publications

References 12 publications

Vehicle detection and tracking using homography-based plane rectification and particle filtering

Vehicle detection and tracking using homography-based plane rectification and particle filtering

Differential evolution based human body pose estimation from point clouds

A self-adaptive edge matching method based on mean shift and its application in video tracking

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