Multi-object tracking using Kalman filter and particle filter

Bukey, Chetan M.; Kulkarni, Shailesh V.; Chavan, Rohini

doi:10.1109/icpcsi.2017.8392001

Cited by 14 publications

(2 citation statements)

References 4 publications

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“…When examining the object in 3D space, applying an attenuation factor in the Marginalized Kalman Filter (MKF) lowers the effect of the prior filter on the present filter. Reference [23] examines the performance regarding extended KF for tracking multiple and single objects with the use of azimuth.The performance of the article filter and KF for 2D visual multi-object tracking in diverse situations is compared in reference [24]. An approach for tracking and detecting moving objects with the use of KF is described in reference [25].…”

Section: Related Work 1: Kalman Filtermentioning

confidence: 99%

Development of Adaptive Tracking using Advance Filter and Selection Features Method

Ghindawi¹,

Kadhim²

2022

IJERAT

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Recently, Kalman filter(KF)-based algorithms of tracking had demonstrated to be effective, however, their efficiency is limited by fixed feature selections and the possibility of model drift. In the presented research, we offer a new adaptive feature selection-based tracking approach that maintains the KF’s excellent discriminating power. Depending on scores of confidence regarding features in every one of frames, the suggested approach might select (automatically)either SIFT feature or the colour feature for the tracking. With a use of KF, a response map related to the SIFT features and color features are retrieved first. The color features that distinguish the luminance from the color are extracted using the Lab color space. Second, the average peak-to-correlation energy is used for the determination of the confidence region and the target's possible location. Finally, a total of 3 criteria have been utilized in order to choose the appropriate feature for present frame in order to execute adaptive tracking. On OTB benchmark datasets, the experimental findings show that the suggested tracker performs better in comparison with other state-of-art techniques.

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Section: Related Work 1: Kalman Filtermentioning

confidence: 99%

Development of Adaptive Tracking using Advance Filter and Selection Features Method

Ghindawi¹,

Kadhim²

2022

IJERAT

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“…Reference [43] analyzes the performance of extended KF using the azimuth to track single and multiple objects, respectively. Reference [44] compares the performance of the Kalman filter and particle filter for two-dimensional visual multi-object tracking in different environments. Reference [45] describes a method for detection and tracking on moving objects using KF.…”

Section: Tracking By Kfmentioning

confidence: 99%

Learning Local–Global Multiple Correlation Filters for Robust Visual Tracking with Kalman Filter Redetection

Zhang

Liu

et al. 2021

Sensors

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Visual object tracking is a significant technology for camera-based sensor networks applications. Multilayer convolutional features comprehensively used in correlation filter (CF)-based tracking algorithms have achieved excellent performance. However, there are tracking failures in some challenging situations because ordinary features are not able to well represent the object appearance variations and the correlation filters are updated irrationally. In this paper, we propose a local–global multiple correlation filters (LGCF) tracking algorithm for edge computing systems capturing moving targets, such as vehicles and pedestrians. First, we construct a global correlation filter model with deep convolutional features, and choose horizontal or vertical division according to the aspect ratio to build two local filters with hand-crafted features. Then, we propose a local–global collaborative strategy to exchange information between local and global correlation filters. This strategy can avoid the wrong learning of the object appearance model. Finally, we propose a time-space peak to sidelobe ratio (TSPSR) to evaluate the stability of the current CF. When the estimated results of the current CF are not reliable, the Kalman filter redetection (KFR) model would be enabled to recapture the object. The experimental results show that our presented algorithm achieves better performances on OTB-2013 and OTB-2015 compared with the other latest 12 tracking algorithms. Moreover, our algorithm handles various challenges in object tracking well.

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Application of Hierarchical Clustering for Object Tracking with a Dynamic Vision Sensor

Bolten

Pohle-Fröhlich

Tönnies

2019

Lecture Notes in Computer Science

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Multi-object tracking using Kalman filter and particle filter

Cited by 14 publications

References 4 publications

Development of Adaptive Tracking using Advance Filter and Selection Features Method

Development of Adaptive Tracking using Advance Filter and Selection Features Method

Learning Local–Global Multiple Correlation Filters for Robust Visual Tracking with Kalman Filter Redetection

Application of Hierarchical Clustering for Object Tracking with a Dynamic Vision Sensor

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