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

Mehmood, Khizer; Jalil, Abdul; Ali, Ahmad; Khan, Baber; Murad, Maria; Khan, Wasim; He, Yigang

doi:10.3390/electronics10010043

Cited by 17 publications

(9 citation statements)

References 40 publications

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“…Some potential ideas should be considered. (1) The idea of adaptive spatial-temporal context [33] can be used to improve the robustness of the tracking part in the TBD. (2)The multi-sensor data fusion [34,35] may be a valuable project for theoretical investigation and practical application in the dim point detection field.…”

Section: Discussionmentioning

confidence: 99%

DP–MHT–TBD: A Dynamic Programming and Multiple Hypothesis Testing-Based Infrared Dim Point Target Detection Algorithm

Lü

Zhang

et al. 2022

Remote Sensing

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The detection and tracking of small targets under low signal-to-clutter ratio (SCR) has been a challenging task for infrared search and track (IRST) systems. Track-before-detect (TBD) is a widely-known algorithm which can solve this problem. However, huge computation costs and storage requirements limit its application. To address these issues, a dynamic programming (DP) and multiple hypothesis testing (MHT)-based infrared dim point target detection algorithm (DP–MHT–TBD) is proposed. It consists of three parts. (1) For each pixel in current frame, the second power optimal merit function-based DP is designed and performed in eight search areas to find the target search area that contains the real target trajectory. (2) In the target search area, the parallel MHT model is designed to save the tree-structured trajectory space, and a two-stage strategy is designed to mitigate the contradiction between the redundant trajectories and the requirements of more trajectories under low SCR. After constant false alarm segmentation of the energy accumulation map, the preliminary candidate points can be obtained. (3) The target tracking method is designed to eliminate false alarms. In this work, an efficient second power optimal merit function-based DP is designed to find the target search area for each pixel, which greatly reduces the trajectory search space. A two-stage MHT model, in which pruning for the tree-structured trajectory space is avoided and all trajectories can be processed in parallel, is designed to further reduce the hypothesis space exponentially. This model greatly reduces computational complexity and saves storage space, improving the engineering application of the TBD method. The DP–MHT–TBD not only takes advantage of the small computation amount of DP and high accuracy of an exhaustive search but also utilizes a novel structure. It can detect a single infrared point target when the SCR is 1.5 with detection probability above 90% and a false alarm rate below 0.01%.

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Section: Discussionmentioning

confidence: 99%

DP–MHT–TBD: A Dynamic Programming and Multiple Hypothesis Testing-Based Infrared Dim Point Target Detection Algorithm

Lü

Zhang

et al. 2022

Remote Sensing

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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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“…Visual object tracking [ 1 , 2 ] is widely used in autonomous driving, medical diagnostics, traffic monitoring, and other fields. Visual tracking aims at estimating the position and scale of the object in subsequent frames, given its state in the first frame.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Feature Interactive Fusion Network for RGBT Tracking

Xiao

Xiong

Meng

et al. 2023

Sensors

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The fusion tracking of RGB and thermal infrared image (RGBT) is paid wide attention to due to their complementary advantages. Currently, most algorithms obtain modality weights through attention mechanisms to integrate multi-modalities information. They do not fully exploit the multi-scale information and ignore the rich contextual information among features, which limits the tracking performance to some extent. To solve this problem, this work proposes a new multi-scale feature interactive fusion network (MSIFNet) for RGBT tracking. Specifically, we use different convolution branches for multi-scale feature extraction and aggregate them through the feature selection module adaptively. At the same time, a Transformer interactive fusion module is proposed to build long-distance dependencies and enhance semantic representation further. Finally, a global feature fusion module is designed to adjust the global information adaptively. Numerous experiments on publicly available GTOT, RGBT234, and LasHeR datasets show that our algorithm outperforms the current mainstream tracking algorithms.

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

Cited by 17 publications

References 40 publications

DP–MHT–TBD: A Dynamic Programming and Multiple Hypothesis Testing-Based Infrared Dim Point Target Detection Algorithm

DP–MHT–TBD: A Dynamic Programming and Multiple Hypothesis Testing-Based Infrared Dim Point Target Detection Algorithm

Efficient Online Object Tracking Scheme for Challenging Scenarios

Multi-Scale Feature Interactive Fusion Network for RGBT Tracking

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