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

Mehmood, Khizer; Jalil, Abdul; Ali, Ahmad; Khan, Baber; Murad, Maria; Cheema, Khalid Mehmood; Milyani, Ahmad H.

doi:10.3390/s21082841

Cited by 6 publications

(4 citation statements)

References 44 publications

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“…Recent literature shows that researchers are continuously trying to handle tracking failure and redetecting the target after failure. Notable articles relevant to tracking failure detection and avoidance occlusion handling are presented in [5,24] and [25,26], respectively. Discriminative correlation filter trackers also suffer from boundary effects.…”

Section: Related Workmentioning

confidence: 99%

Multiple Cues-Based Robust Visual Object Tracking Method

et al. 2022

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Visual object tracking is still considered a challenging task in computer vision research society. The object of interest undergoes significant appearance changes because of illumination variation, deformation, motion blur, background clutter, and occlusion. Kernelized correlation filter- (KCF) based tracking schemes have shown good performance in recent years. The accuracy and robustness of these trackers can be further enhanced by incorporating multiple cues from the response map. Response map computation is the complementary step in KCF-based tracking schemes, and it contains a bundle of information. The majority of the tracking methods based on KCF estimate the target location by fetching a single cue-like peak correlation value from the response map. This paper proposes to mine the response map in-depth to fetch multiple cues about the target model. Furthermore, a new criterion based on the hybridization of multiple cues i.e., average peak correlation energy (APCE) and confidence of squared response map (CSRM), is presented to enhance the tracking efficiency. We update the following tracking modules based on hybridized criterion: (i) occlusion detection, (ii) adaptive learning rate adjustment, (iii) drift handling using adaptive learning rate, (iv) handling, and (v) scale estimation. We integrate all these modules to propose a new tracking scheme. The proposed tracker is evaluated on challenging videos selected from three standard datasets, i.e., OTB-50, OTB-100, and TC-128. A comparison of the proposed tracking scheme with other state-of-the-art methods is also presented in this paper. Our method improved considerably by achieving a center location error of 16.06, distance precision of 0.889, and overlap success rate of 0.824.

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Section: Related Workmentioning

confidence: 99%

Multiple Cues-Based Robust Visual Object Tracking Method

et al. 2022

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“…To handle occlusion and deformation robustly, several strategies [40][41][42][43][44] have been used. In deep learning methods, data collection and annotation is the most straightforward way, while it seems impossible to collect data covering all potential occlusion and deformation, even for large-scale datasets.…”

Section: Related Work 21 Occlusion and Deformation Handling In Visual...mentioning

confidence: 99%

Occlusion and Deformation Handling Visual Tracking for UAV via Attention-Based Mask Generative Network

et al. 2022

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Although the performance of unmanned aerial vehicle (UAV) tracking has benefited from the successful application of discriminative correlation filters (DCF) and convolutional neural networks (CNNs), UAV tracking under occlusion and deformation remains a challenge. The main dilemma is that challenging scenes, such as occlusion or deformation, are very complex and changeable, making it difficult to obtain training data covering all situations, resulting in trained networks that may be confused by new contexts that differ from historical information. Data-driven strategies are the main direction of current solutions, but gathering large-scale datasets with object instances under various occlusion and deformation conditions is difficult and lacks diversity. This paper proposes an attention-based mask generation network (AMGN) for UAV-specific tracking, which combines the attention mechanism and adversarial learning to improve the tracker’s ability to handle occlusion and deformation. After the base CNN extracts the deep features of the candidate region, a series of masks are determined by the spatial attention module and sent to the generator, and the generator discards some features according to these masks to simulate the occlusion and deformation of the object, producing more hard positive samples. The discriminator seeks to distinguish these hard positive samples while guiding mask generation. Such adversarial learning can effectively complement occluded and deformable positive samples in the feature space, allowing to capture more robust features to distinguish objects from backgrounds. Comparative experiments show that our AMGN-based tracker achieves the highest area under curve (AUC) of 0.490 and 0.349, and the highest precision scores of 0.742 and 0.662, on the UAV123 tracking benchmark with partial and full occlusion attributes, respectively. It also achieves the highest AUC of 0.555 and the highest precision score of 0.797 on the DTB70 tracking benchmark with the deformation attribute. On the UAVDT tracking benchmark with the large occlusion attribute, it achieves the highest AUC of 0.407 and the highest precision score of 0.582.

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“…A Kalman filter is used in various tracking algorithms for occlusion handling [45][46][47][48][49]. Kaur et al [50] suggested a real-time tracking approach using a fractional-gain Kalman filter for nonlinear systems.…”

Section: Related Workmentioning

confidence: 99%

Efficient Online Object Tracking Scheme for Challenging Scenarios

Mehmood

Ali

Jalil

et al. 2021

Sensors

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Visual object tracking (VOT) is a vital part of various domains of computer vision applications such as surveillance, unmanned aerial vehicles (UAV), and medical diagnostics. In recent years, substantial improvement has been made to solve various challenges of VOT techniques such as change of scale, occlusions, motion blur, and illumination variations. This paper proposes a tracking algorithm in a spatiotemporal context (STC) framework. To overcome the limitations of STC based on scale variation, a max-pooling-based scale scheme is incorporated by maximizing over posterior probability. To avert target model from drift, an efficient mechanism is proposed for occlusion handling. Occlusion is detected from average peak to correlation energy (APCE)-based mechanism of response map between consecutive frames. On successful occlusion detection, a fractional-gain Kalman filter is incorporated for handling the occlusion. An additional extension to the model includes APCE criteria to adapt the target model in motion blur and other factors. Extensive evaluation indicates that the proposed algorithm achieves significant results against various tracking methods.

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

Cited by 6 publications

References 44 publications

Multiple Cues-Based Robust Visual Object Tracking Method

Multiple Cues-Based Robust Visual Object Tracking Method

Occlusion and Deformation Handling Visual Tracking for UAV via Attention-Based Mask Generative Network

Efficient Online Object Tracking Scheme for Challenging Scenarios

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