Combining Spatio-Temporal Context and Kalman Filtering for Visual Tracking

Yang, Haoran; Wang, Juanjuan; Yi, Miao; Yang, Yue; Zhao, Zhihua; Wang, Zhigang; Sun, Qian; Wu, Dapeng

doi:10.3390/math7111059

Cited by 11 publications

(11 citation statements)

References 32 publications

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“…Yang et al [26] proposed an enhanced STC tracker to address occlusion by incorporating a PSR-based occlusion feedback mechanism for the model and scale update in the STC framework. Yang et al [27] proposed an improved STC tracker to address occlusion through incorporating a Kalman filter for prediction of target location and uses Euclidean distance to detect occlusion. Zhang et al [28] proposed a motion aware correlation filter (MACF) which predicts position and scale of the target in the next frame by utilizing instantaneous motion estimation.…”

Section: Related Workmentioning

confidence: 99%

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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%

“…where I ij is the pixel value and M * N is the size of image. Learning rate is adjusted as given in (27).…”

Section: Adaptive Learning Ratementioning

confidence: 99%

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

et al. 2020

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“…Kalman filters are widely utilized for occlusion handling in various trackers [ 34 , 35 , 36 , 37 , 38 ]. Yang et al [ 39 ] proposed an improved STC algorithm and combined the Kalman filter with STC making it more robust and used Euclidean distance to detect occlusion. Mehmood et al [ 40 ] proposed a tracking algorithm similar to [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al [ 39 ] proposed an improved STC algorithm and combined the Kalman filter with STC making it more robust and used Euclidean distance to detect occlusion. Mehmood et al [ 40 ] proposed a tracking algorithm similar to [ 39 ]. In their implementation, they have incorporated context-aware formulation and combined Kalman filter in the STC framework.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Mehmood

Jalil

Ali

et al. 2021

Sensors

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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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“…A typical scenario of visual tracking is to track an unknown object in subsequent image frames by giving the initial state of a target in the first frame of the video. In the past few decades, the visual object tracking technology has made significant progress [1][2][3][4][5][6][7][8][9][10]. These methods are very effective for short-term tracking tasks, which is that the tracked object is almost always in the field of view.…”

Section: Introductionmentioning

confidence: 99%

Long-term target tracking combined with re-detection

Zhao

Wang

Yang

et al. 2020

Preprint

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The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Learning Adaptive Discriminative Correlation Filters (LADCF) tracking algorithm with a re-detection component based on the SVM model. The LADCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

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Combining Spatio-Temporal Context and Kalman Filtering for Visual Tracking

Cited by 11 publications

References 32 publications

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

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

Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Long-term target tracking combined with re-detection

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