Particle Filter Based Object Tracking with Sift and Color Feature

Fazli, Saeid; Pour, Hamed Moradi; Bouzari, Hamed

doi:10.1109/icmv.2009.47

Cited by 40 publications

(18 citation statements)

References 7 publications

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“…SIFT features, originally proposed by Lowe [30], are normally used in problems of panoramas reconstruction [31], face identification/authentication [32][33][34], and most importantly, visual object tracking [35]. However, its diverse and distinctive nature helps researchers to use it in other application areas.…”

Section: Sift Featuresmentioning

confidence: 99%

Automated colon cancer detection using hybrid of novel geometric features and some traditional features

Rathore

Hussain

Khan

2015

Computers in Biology and Medicine

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Section: Sift Featuresmentioning

confidence: 99%

Automated colon cancer detection using hybrid of novel geometric features and some traditional features

Rathore

Hussain

Khan

2015

Computers in Biology and Medicine

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“…Robust vision tracking technologies are utilized widely, particularly in applications such as automated surveillance, traffic monitoring, robot guidance, humancomputer interface and vehicle navigation [1 -5]. A great variety of powerful object tracking algorithms have generated, such as Mean-shift [6], SVM tracker [7], Kanade-Lucas-Tomasi (KLT) [8], Shi-TomasiKanade (STK) [9], Hidden Markov Model (HMM) [10], Particle Filter (PF) tracker [11], Kalman Filter (KF) tracker [12], SIFT tracker [13] and SURF tracker [14]. Scale Invariant transform Feature (SIFT) [15] recently gained more attention due to its steady feature extraction, robustness to partial occlusion, scaleinvariance and rotation-invariance.…”

Section: Introductionmentioning

confidence: 99%

A Robust Object Tracking Algorithm Based on Surf and Kalman Filter

Yin¹,

Peng²,

Chai³

et al. 2013

Intelligent Automation & Soft Computing

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In this paper, an efficient robust object tracking approach based on SURF and Kalman Filter is proposed. SURF as an outstanding local invariant feature is employed. Based on the SURF feature, a SURF match method is proposed. A combination method using an ingenious method and KF is used to predict the possible region, in which the tracking object may appear. Only in this region, SURF features are extracted and matched. It can significantly reduce the computational complexity. A histogram-based re-match process is employed to dislodge failure tracking after SURF matching. To verify the performance of the proposed algorithm, several comparative experiments are conducted. The results reveal that the proposed method achieves better performance and accuracy than conventional methods.

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“…An appearance model based on superpixels was used to distinguish the object from its background. The scale-invariant feature transform (SIFT) [17] is a widely used local feature extraction algorithm; some approaches [18][19][20] use it to match regions of interest between frames in a tracking framework. Static and motion saliency features [21,22] and corner features [23] have also been commonly used in object tracking.…”

Section: Object Trackingmentioning

confidence: 99%

Robust tracking-by-detection using a selection and completion mechanism

Fan

Zhang

et al. 2017

Comp. Visual Media

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It is challenging to track a target continuously in videos with long-term occlusion, or objects which leave then re-enter a scene. Existing tracking algorithms combined with onlinetrained object detectors perform unreliably in complex conditions, and can only provide discontinuous trajectories with jumps in position when the object is occluded. This paper proposes a novel framework of tracking-by-detection using selection and completion to solve the abovementioned problems. It has two components, tracking and trajectory completion. An offline-trained object detector can localize objects in the same category as the object being tracked. The object detector is based on a highly accurate deep learning model. The object selector determines which object should be used to re-initialize a traditional tracker. As the object selector is trained online, it allows the framework to be adaptable. During completion, a predictive non-linear autoregressive neural network completes any discontinuous trajectory. The tracking component is an online real-time algorithm, and the completion part is an after-theevent mechanism. Quantitative experiments show a significant improvement in robustness over prior stateof-the-art methods.

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Particle Filter Based Object Tracking with Sift and Color Feature

Cited by 40 publications

References 7 publications

Automated colon cancer detection using hybrid of novel geometric features and some traditional features

Automated colon cancer detection using hybrid of novel geometric features and some traditional features

A Robust Object Tracking Algorithm Based on Surf and Kalman Filter

Robust tracking-by-detection using a selection and completion mechanism

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