Smartphone based indoor localization and tracking model using bat algorithm and Kalman filter

Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.

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RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers

IEEE Journal of Selected Topics in Signal Processing ◽

10.1109/jstsp.2009.2032309 ◽

2009 ◽

Vol 3 (5) ◽

pp. 860-873 ◽

Cited By ~ 163

Author(s):

Anindya S. Paul ◽

Eric A. Wan

Keyword(s):

Indoor Localization ◽

Sigma Point ◽

Localization And Tracking

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An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm

10.54216/jcim.060203 ◽

2021 ◽

pp. 101-107

Author(s):

Mohammad Alshehri ◽

Keyword(s):

Real Time ◽

Indoor Localization ◽

Initial Position ◽

Swarm Optimization ◽

The Real ◽

Assisted Navigation ◽

Glowworm Swarm Optimization ◽

Tracking Process ◽

Wide Range ◽

Localization And Tracking

Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.

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An improved tracking model based on self-adaptive Kalman filter

Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications ◽

10.2991/icmmita-16.2016.290 ◽

2016 ◽

Author(s):

Meng Lian ◽

Yi Sun

Keyword(s):

Kalman Filter ◽

Adaptive Kalman Filter ◽

Model Based ◽

Tracking Model ◽

Self Adaptive

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Marker-Free Indoor Localization and Tracking of Multiple Users in Smart Environments Using a Camera-Based Approach

Distributed, Ambient, and Pervasive Interactions - Lecture Notes in Computer Science ◽

10.1007/978-3-642-39351-8_38 ◽

2013 ◽

pp. 349-357 ◽

Cited By ~ 3

Author(s):

Andreas Braun ◽

Tim Dutz ◽

Michael Alekseew ◽

Philipp Schillinger ◽

Alexander Marinc

Keyword(s):

Indoor Localization ◽

Smart Environments ◽

Multiple Users ◽

Marker Free ◽

Localization And Tracking

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Estimation for Motion in Tracking and Detection Objects with Kalman Filter

Dynamic Data Assimilation - Beating the Uncertainties ◽

10.5772/intechopen.92863 ◽

2020 ◽

Author(s):

Afef Salhi ◽

Fahmi Ghozzi ◽

Ahmed Fakhfakh

Keyword(s):

Computer Vision ◽

Kalman Filter ◽

Visual Motion ◽

Optimal Solution ◽

Block Matching ◽

Detection And Tracking ◽

Tracking Object ◽

Tracking Objects ◽

Localization And Tracking ◽

Prediction Parameters

The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems.

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