Iterative particle filter for visual tracking

Fan, Zhaoqing; Ji, Hongbing; Zhang, Yongquan

doi:10.1016/j.image.2015.07.001

Cited by 28 publications

(8 citation statements)

References 29 publications

(27 reference statements)

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“…The Euclidean distance between the ground truth and each tracker path [17] is illustrated in Fig. 10.…”

Section: Experimental Results and Considerationsmentioning

confidence: 99%

“…A conventional particle filter, also known as a condensation algorithm [16], is highly robust to clutter. Although it is a robust tracker, it suffers from high computational burden and inefficient particle distribution [17]. High computational burden will not affect the accuracy of tracking but can extend a system's capacity; thus, the default number of particles within a particle filter method needs to be reduced.…”

Section: Introductionmentioning

confidence: 99%

“…In [21], a firefly algorithm-based particle filter was introduced that achieved a robust tracking. In [17], an iterative particle filter that increased the tracking accuracy is presented. However, it was not able to affect the computational burden.…”

Section: Introductionmentioning

confidence: 99%

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Direction-Based Modified Particle Filter for Vehicle Tracking

Yıldırım¹,

Ince²,

Salman³

et al. 2016

ETRI J

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This research proposes a modified particle filter to increase the accuracy of vehicle tracking in a noisy and occluded medium. In our proposed method for vehicle tracking, the direction angle of a target vehicle is calculated. The angular difference between the motion direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted depending on their angular distance to the motion direction. Those particles moving in a direction similar to that of the target vehicle are assigned larger weights; this, in turn, increases their probability in a given likelihood function (part of the process of estimation of a target's state parameters). The proposed method is compared against a condensation algorithm. Our results show that the proposed method improves the stability of a particle filter tracker and decreases the particle consumption.

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“…The Euclidean distance between the ground truth and each tracker path [17] is illustrated in Fig. 10.…”

Section: Experimental Results and Considerationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Direction-Based Modified Particle Filter for Vehicle Tracking

Yıldırım¹,

Ince²,

Salman³

et al. 2016

ETRI J

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show abstract

“…An iterative particle filter algorithm was presented which is able to increase the accuracy but could not reduce the computational burden [20]. In [21], authors proposed a multifeature target representation with particle filter, although, it has shown success in accuracy, the computational load remained high.…”

Section: Burak Merdenyan University Of York United Kingdommentioning

confidence: 99%

Visual Vehicle Tracking Using Direction based Particle Filter with Online Threshold Adaptation

Merdenyan¹,

Eren²,

Batu³

2016

Fourth International Conference on Advances in Computing, Electronics and Communication - ACEC 2016

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In various applications of computer vision, nonlinearity and non-Gaussianity must be considered to have an accurate and realistic modeling. In this sense, particle filter is preferred for tracking. In this paper, a modified particle filter which takes the direction of the vehicle into account and distributes the particles according an automatic thresholding scheme is used. After obtaining the vehicle direction, particles are weighted according to their angular similarities to the vehicle. Particles having angular distance greater than a threshold are eliminated. Thus, the remained particles that are moving in same or similar direction with vehicle, increase their own probability of likelihood. This threshold is decided online while the algorithm runs. Depending on the number of particles used, this scheme increases or decreases the threshold. This scheme prevents the system from failures due to insufficient number of particles and also from using excessive number of particles. Proposed algorithm is compared with condensation algorithm and Camshift algorithm. According to the results, proposed algorithm surpasses the other algorithms in terms of tracking period and precision.

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“…In light of these difficulties, several contemporary state of the art deep learning-based tracking models have been developed as generic object trackers in an effort to obviate the need for online training and to also improve the generalizability of the tracker. [10] applies a regressionbased approach to train a generic tracker, GOTURN, offline to learn a generic relationship between appearance and motion; several deep techniques additionally incorporate motion and occlusion models, including particle filtering methods [11] and optical flow [12].…”

Section: Introductionmentioning

confidence: 99%

Deep Siamese Networks with Bayesian Non-parametrics for Video Object Tracking

Rhodes

Goel

2019

Advances in Intelligent Systems and Computing

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We present a novel algorithm utilizing a deep Siamese neural network as a general object similarity function in combination with a Bayesian optimization (BO) framework to encode spatio-temporal information for efficient object tracking in video. In particular, we treat the video tracking problem as a dynamic (i.e. temporally-evolving) optimization problem. Using Gaussian Process priors, we model a dynamic objective function representing the location of a tracked object in each frame. By exploiting temporal correlations, the proposed method queries the search space in a statistically principled and efficient way, offering several benefits over current state of the art video tracking methods.

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Iterative particle filter for visual tracking

Cited by 28 publications

References 29 publications

Direction-Based Modified Particle Filter for Vehicle Tracking

Direction-Based Modified Particle Filter for Vehicle Tracking

Visual Vehicle Tracking Using Direction based Particle Filter with Online Threshold Adaptation

Deep Siamese Networks with Bayesian Non-parametrics for Video Object Tracking

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