A color-based particle filter for multiple object tracking in an outdoor environment

Sugandi, Budi; Kim, Hyoungseop; Tan, Joo Koi; Ishikawa, Seiji

doi:10.1007/s10015-010-0762-2

Cited by 16 publications

(8 citation statements)

References 13 publications

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“…In this paper occlusions are also predicted by the predicted distance between the objects as [18].The threshold is set according to (15) in [18]. When the distance between the object is smaller than the threshold occlusions occurs between them.We can determine which object is occluded by comparing the features of the region with the two objects.After determine the object occluded the weights are set to zero in process of particle filter resample.Eventhough we can not distinguish the occluded objects we can use the GMM to detect them after the pass away from each other.After detect the object which is we want to track we update the particles with features,sizes and positions in this region and track it by means of CHOG particle filter.…”

Section: B Occlusion Handlingmentioning

confidence: 99%

“…Deterministic methods localize the tracked object in each frame by iteratively searching for a region which maximizes the similarity measure between this region and target window.Comaniciu et al [3], [14] employed mean shift algorithm for object tracking.Mean shift algorithm is computationally efficient.However algorithms based on mean shift may converges to a local maximum,they are also sensitive to background distracts,occlusions,and objects quick moving.Among various of stochastic methods,Particle filter [11], [12], [15], [16], [17] is widely used and makes the tracking system robustly.In [12] the author introduced switching probabilistic principal component analysis model to update the templates while in [18] color histogram was replace by the sum of color histogram of target and estimate's.Some of color particle filter introduce model update as [18], while this type of methods are subject to tracking drift.We introduce kalman filter to do model update.In [19] the author proposed multiple objects detection and tracking, object detection was based on Bayesian estimation.Object detection based on Bayesian estimation is delayed by a few frames. In this article we introduced GMM and Bhattacharyya distance to do objects appearance detection.…”

Section: Introductionmentioning

confidence: 99%

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Model Update Particle Filter for Multiple Objects Detection and Tracking

Zhao

Pei

2011

2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing

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Multiple objects tracking is a challenging task. This article presents an algorithm which can detect and track multiple objects, and update target model automatically.The contributions of this paper as follow: Firstly,we use color histogram(HC) and histogram of orientated gradients(HOG) to represent the objects,model update is realized under the frame of kalman filter and gaussian model; secondly we use Gaussian Mixture Model(GMM) and Bhattacharyya distance to detect object appearance. Particle filter with combined features and model update mechanism can improve tracking effects.Experiments on video sequences demonstrate that multiple objects tracking based on improved algorithm have good performance.

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Section: B Occlusion Handlingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model Update Particle Filter for Multiple Objects Detection and Tracking

Zhao

Pei

2011

2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing

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show abstract

“…In order to improve the quality of tracking, K. Nummiaroa et al have proposed to model the objects by adaptive color distributions, combined with edge-based image features [19]. So as to track multiple objects, B. Sugandi et al have also implemented the color as a discriminative feature [20]. However, the color histogram degrades the tracking performance in case of occlusion or when the background and the target have similar color distribution.…”

Section: Related Workmentioning

confidence: 99%

Visual Tracking Using Particle Filter Based on Gabor Features

Lahraichi¹,

Housni²,

Mbarki³

2018

IJIES

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Abstract:Object tracking is a crucial step in video analysis, as well as detection and recognition. Particle filter, based on color histogram, is considered among multiple approaches that prove their effectiveness in this domain. It is apparent that if an object and its background, or more objects to be tracked, having nearly the same color histogram, the quality of tracking will be negatively affected. Therefore, combining color histogram and texture features for pixels in motion is considered a potential solution to this issue. In this paper, we propose a particle filter tracking method consisting of two stages: at the prediction stage, the state space model is represented by Box-Muller transform, and at the correction stage, the observation likelihood model is calculated in three steps: firstly, the background subtraction is used to extract the moving objects in order to reduce the impact of background pixels on color histogram. Secondly, the texture Gabor features are computed using moving pixels to distinguish between objects having the same color histogram. Finally, the histogram and texture features are combined to define a likelihood observation model. The proposed method is implemented using several video sequences. The visual and numerical results obtained are compared with those of traditional particle filter method, and show that our method is very efficient particularly in case of similar objects, and clearly improves the quality of tracking.

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“…The method uses a particle filter where a multi-mode anisotropic mean shift is embedded to improve the initial particles. Budi Sugandi et al 19 proposed a method for multiple object tracking based on color features. Whist Jacek Czyz et al 20 proposed that target detection and deletion are embedded in the color particle filter without relaying on an external track initialization and cancelation algorithm to track multiple targets.…”

Section: Introductionmentioning

confidence: 99%

Model Update Particle Filter for Multiple Objects Detection and Tracking

Zhao¹,

Pei²

2012

IJCIS

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Multiple objects tracking is a challenging task. This article presents an algorithm which can detect and track multiple objects, and update target model automatically. The contributions of this paper as follow: Firstly,we also use color histogram(CH) and histogram of orientated gradients(HOG) to represent the objects, model update is realized by kalman filter and gaussian model; secondly we use Gaussian Mixture Model(GMM) and Bhattacharyya distance to detect object appearance. Particle filter with combined features and model update mechanism can improve tracking results. Experiments on video sequences demonstrate that the method presented in this paper can realize multiple objects detection and tracking.

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A color-based particle filter for multiple object tracking in an outdoor environment

Cited by 16 publications

References 13 publications

Model Update Particle Filter for Multiple Objects Detection and Tracking

Model Update Particle Filter for Multiple Objects Detection and Tracking

Visual Tracking Using Particle Filter Based on Gabor Features

Model Update Particle Filter for Multiple Objects Detection and Tracking

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