Recent Advances in Object Detection and Tracking for High Resolution Video: Overview and State-of-the-Art

Rajjak, Shaikh Shakil Abdul; Kureshi, A. K.

doi:10.1109/iccubea47591.2019.9128812

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…That said, these methods are evaluated on two kinds of ground truths: HBB and OBB ground truths (6) . Each detected bounding box has a corresponding confidence score for the two tasks.…”

Section: Methodsmentioning

confidence: 99%

Computer Vision Application in Object Detection and Tracking for Aerial Surveillance

Nandibewoor,

Prateek,

Sakaray

et al. 2023

IJST

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Objectives: Computer vision duties like object detection, tracking, and counting are significant for surveillance. Factors like altitude, camera angle, occlusion, and motion blur make it a more challenging task. To present a method to overcome all these factors and implement surveillance quickly and accurately for smaller and larger object aspect ratios. Methods: Horizontal Bounding Boxes and Oriented Bounding Boxes (HBB and OBB) are evaluated on two ground truths respectively. PASCAL VOC 07 metric is adopted to calculate the mean average precision. Constructed on the score, the original implementation of Mask R-CNN includes the application of a mask head to the highest-scoring 100 HBBs. Subsequently, the mask head was extended to all HBBs remaining after the process of Non-Maximum Suppression. This modification allowed the evaluation of Mask R-CNN, Cascade Mask R-CNN, and Hybrid Task Cascade methods on a wider range of bounding boxes. Findings: In summary, this research explores and compares different approaches and techniques in the field of object detection, particularly focusing on oriented object detection and the challenges posed by geometric variations. Furthermore, it addresses the impact of different models, such as Mask R-CNN, Faster R-CNN OBB + RoI Transformer, and Faster R-CNN OBB + Dpool, on performance. Additionally, it highlights the importance of handling numerical instability caused by extremely small instances. The research findings are visually presented in Figure 2, providing a clear representation of the performance of various networks. Novelty: The study summarizes the findings of existing research papers and identifies research gaps. The performance parameters of the various algorithms and analysis for various networks show the evolution of various methods over the years. With changes in the network, like mask transferring and dataset, the accuracy for smaller, bigger objects and speed of execution are affected, are explained in results and discussions as well as the conclusions.

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“…That said, these methods are evaluated on two kinds of ground truths: HBB and OBB ground truths (6) . Each detected bounding box has a corresponding confidence score for the two tasks.…”

Section: Methodsmentioning

confidence: 99%

Computer Vision Application in Object Detection and Tracking for Aerial Surveillance

Nandibewoor,

Prateek,

Sakaray

et al. 2023

IJST

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“…OD for patient monitoring has not been studied widely in the literature. Existing studies have incompatible prerequisites like highresolution video input [8], or employ off-the-shelf OD methods [9] on unblurred data. The quasi-industry standard for stateof-the-art OD method (also used in [9]) is the YOLO family of models [10], [11].…”

Section: Related Workmentioning

confidence: 99%

Video Object Detection for Privacy-Preserving Patient Monitoring in Intensive Care

Emberger,

Boss,

Baumann

et al. 2023

2023 10th IEEE Swiss Conference on Data Science (SDS)

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Patient monitoring in intensive care units, although assisted by biosensors, needs continuous supervision of staff. To reduce the burden on staff members, IT infrastructures are built to record monitoring data and develop clinical decision support systems. These systems, however, are vulnerable to artifacts (e.g. muscle movement due to ongoing treatment), which are often indistinguishable from real and potentially dangerous signals.Video recordings could facilitate the reliable classification of biosignals using object detection (OD) methods to find sources of unwanted artifacts. Due to privacy restrictions, only blurred videos can be stored, which severely impairs the possibility to detect clinically relevant events such as interventions or changes in patient status with standard OD methods. Hence, new kinds of approaches are necessary that exploit every kind of available information due to the reduced information content of blurred footage and that are at the same time easily implementable within the IT infrastructure of a normal hospital. In this paper, we propose a new method for exploiting information in the temporal succession of video frames. To be efficiently implementable using off-the-shelf object detectors that comply with given hardware constraints, we repurpose the image color channels to account for temporal consistency, leading to an improved detection rate of the object classes. Our method outperforms a standard YOLOv5 baseline model by +1.7% mAP@.5 while also training over ten times faster on our proprietary dataset. We conclude that this approach has shown effectiveness in the preliminary experiments and holds potential for more general video OD in the future.

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Target tracking algorithm based on improved kernel correlation filtering

Shi

Hou

et al. 2021

2021 China Automation Congress (CAC)

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Recent Advances in Object Detection and Tracking for High Resolution Video: Overview and State-of-the-Art

Cited by 4 publications

References 40 publications

Computer Vision Application in Object Detection and Tracking for Aerial Surveillance

Computer Vision Application in Object Detection and Tracking for Aerial Surveillance

Video Object Detection for Privacy-Preserving Patient Monitoring in Intensive Care

Target tracking algorithm based on improved kernel correlation filtering

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