Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking

Ali, Ahmad; Jalil, Abdul; Javed, Ali; Iftikhar, Muhammad Aksam; Hussain, M.

doi:10.1007/s11760-014-0612-0

Cited by 19 publications

(11 citation statements)

References 53 publications

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“…They incorporated the Kalman filter in the LCT framework for occlusion handling and PSR of the response map for occlusion detection. Ali et al [36] proposed a tracking algorithm that combines the mean-shift tracker, Kalman filter, and correlation filter heuristically. It updates the template based on the change in the appearance model of the target and computes similarity for each forthcoming frame based on the current frame similarity value.…”

Section: Related Workmentioning

confidence: 99%

Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking

et al. 2020

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Object tracking is still an intriguing task as the target undergoes significant appearance changes due to illumination, fast motion, occlusion and shape deformation. Background clutter and numerous other environmental factors are other major constraints which remain a riveting challenge to develop a robust and effective tracking algorithm. In the present study, an adaptive Spatio-temporal context (STC)-based algorithm for online tracking is proposed by combining the context-aware formulation, Kalman filter, and adaptive model learning rate. For the enhancement of seminal STC-based tracking performance, different contributions were made in the proposed study. Firstly, a context-aware formulation was incorporated in the STC framework to make it computationally less expensive while achieving better performance. Afterwards, accurate tracking was made by employing the Kalman filter when the target undergoes occlusion. Finally, an adaptive update scheme was incorporated in the model to make it more robust by coping with the changes of the environment. The state of an object in the tracking process depends on the maximum value of the response map between consecutive frames. Then, Kalman filter prediction can be updated as an object position in the next frame. The average difference between consecutive frames is used to update the target model adaptively. Experimental results on image sequences taken from Template Color (TC)-128, OTB2013, and OTB2015 datasets indicate that the proposed algorithm performs better than various algorithms, both qualitatively and quantitatively.

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Section: Related Workmentioning

confidence: 99%

Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking

et al. 2020

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“…Even though the mean-shift object tracking technique is well-performed over sequences with comparatively slight object displacement, its performance cannot be guaranteed in the case where objects suffers full or partial occlusions. Kalman filter [22,23] and particle filter [24,25] algorithms are considered along mean-shift algorithms for improving the tracking performance under partial occlusion. The approach by Bhat et al [24] uses a fusion of color and KAZE features [26] in the particle filter framework to give an effective result in different environments for tracking the target.…”

Section: Related Workmentioning

confidence: 99%

“…The weight of Gaussian distribution is represented by , and sum of all weights is equal to 1. Equation (22) describes the process of Gaussian mixture model (GMM). = { ( ) } … (21) samples were reproduced in according to the weight of ( ) .…”

Section: (C)mentioning

confidence: 99%

An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering

Chang

Pare

Meena

et al. 2020

BDCC

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At present, traditional visual-based surveillance systems are becoming impractical, inefficient, and time-consuming. Automation-based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted resampling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.

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“…If we know the motion dynamics of the target, it is possible to predict the most likely location of the target in the next frame, thus narrowing down the search. Kalman filter predicts the location of the target in case of occlusion, resulting in robust tracking [8]. For modelling the dynamics, we use the equations of the motion proposed by Ahmad.…”

Section: Kalman Extended Spatio-temporal Context Learningmentioning

confidence: 99%

Real time eye tracking using Kalman extended spatio-temporal context learning

et al. 2017

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Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking

Cited by 19 publications

References 53 publications

Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking

Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking

An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering

Real time eye tracking using Kalman extended spatio-temporal context learning

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