High-speed moving target tracking of multi-camera system with overlapped field of view

Yan, Mi; Zhao, Yuejin; Liu, Ming; Kong, Lingqin; Dong, Liquan

doi:10.1007/s11760-021-01867-9

Cited by 4 publications

(11 citation statements)

References 24 publications

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“…Two monocular cameras allowed them to construct a stereo system for 3D trajectories. Yan et al [32] used four fixed monocular cameras for handover problems in computer vision to track a skater as the skater escapes the field of view (FOV) of one camera to another. Gionfrida et al [13] used a single monocular camera to capture the participant's images to develop a markerless hand motion capture system.…”

Section: Monocular Camerasmentioning

confidence: 99%

“…If the system does interact with its environment, such as tracking in sports [32], a biomechanical assessment [13], or observing pedestrian movements, a monocular camera is sufficient. • If depth information is required, uncalibrated stereo methods can be used with either a moving camera [4] or multiple monocular cameras [16].…”

mentioning

confidence: 99%

“…Object Tracking Benchmark (OTB) [53] is another single-object tracking dataset. OTB-50, consisting of 50 difficult target objects out of 100 targets from OTB [53], was used by Yan et al [32] to evaluate their trackers. OTB has annotations consisting of 11 attributes: illumination variation, scale variation, occlusion, deformation, motion blur, fast motion, in-plane rotation, out-of-plane rotation, out-of-view, background clutters, and low resolution [53].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Object Tracking Using Computer Vision: A Review

Kadam,

Fang,

Zou

2024

Computers

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Object tracking is one of the most important problems in computer vision applications such as robotics, autonomous driving, and pedestrian movement. There has been a significant development in camera hardware where researchers are experimenting with the fusion of different sensors and developing image processing algorithms to track objects. Image processing and deep learning methods have significantly progressed in the last few decades. Different data association methods accompanied by image processing and deep learning are becoming crucial in object tracking tasks. The data requirement for deep learning methods has led to different public datasets that allow researchers to benchmark their methods. While there has been an improvement in object tracking methods, technology, and the availability of annotated object tracking datasets, there is still scope for improvement. This review contributes by systemically identifying different sensor equipment, datasets, methods, and applications, providing a taxonomy about the literature and the strengths and limitations of different approaches, thereby providing guidelines for selecting equipment, methods, and applications. Research questions and future scope to address the unresolved issues in the object tracking field are also presented with research direction guidelines.

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Section: Monocular Camerasmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Object Tracking Using Computer Vision: A Review

Kadam,

Fang,

Zou

2024

Computers

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“…Compared with single cameras, target detection and tracking with multi-camera cooperation has unique advantages in many aspects. Therefore, related technologies have attracted more and more researchers' interest [7]. Khan SM and Shah M studied a method of multiple points of view, where all cameras obtain a sequence of images collected in a collaborative framework, and at the same time multiple prospects of likelihood information fuse together, using two-dimensional structure positioning to track a moving object in the scene, to solve the problem of shade and improve robustness [8].…”

Section: Related Workmentioning

confidence: 99%

Sea-Surface Target Visual Tracking with a Multi-Camera Cooperation Approach

Rao

Chen

et al. 2022

Sensors

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Cameras are widely used in the detection and tracking of moving targets. Compared to target visual tracking using a single camera, cooperative tracking based on multiple cameras has advantages including wider visual field, higher tracking reliability, higher precision of target positioning and higher possibility of multiple-target visual tracking. With vast ocean and sea surfaces, it is a challenge using multiple cameras to work together to achieve specific target tracking and detection, and it will have a wide range of application prospects. According to the characteristics of sea-surface moving targets and visual images, this study proposed and designed a sea-surface moving-target visual detection and tracking system with a multi-camera cooperation approach. In the system, the technologies of moving target detection, tracking, and matching are studied, and the strategy to coordinate multi-camera cooperation is proposed. The comprehensive experiments of cooperative sea-surface moving-target visual tracking show that the method used in this study has improved performance compared with contrapositive methods, and the proposed system can meet the needs of multi-camera cooperative visual tracking of moving targets on the sea surface.

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“…At present, scholars have carried out research on fusion system of multiple depth cameras. For example, Yan et al [26] used four depth cameras to build a tracking system and used the FOV method to track and match the motion characteristics of objects, so as to solve the problem of object switching in overlapping areas between multiple cameras; In order to solve the problem of blurred image when detecting parts with a single camera, Wan [27] studied the methods of multicamera calibration and image mosaic and used the good panoramic mosaic image for detection. Hayat et al [28] proposed a cost-effective 360 °panorama generation system, which could process single view and three-dimensional panoramas, and eliminated the splicing gap of overlapping areas between adjacent cameras.…”

Section: Application Case Of Robot Safety Protectionmentioning

confidence: 99%

Human Position Detection Based on Depth Camera Image Information in Mechanical Safety

Zhou

Ren

Zhang

et al. 2022

Advances in Mathematical Physics

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The devices used for human position detection in mechanical safety mainly include safety light curtain, safety laser scanner, safety pad, and vision system. However, these devices may be bypassed when used, and human or equipment cannot be distinguished. To solve this problem, a depth camera is proposed as a human position detection device in mechanical safety. The process of human position detection based on depth camera image information is given; it mainly includes image information acquisition, human presence detection, and distance measurement. Meanwhile, a human position detection method based on Intel RealSense depth camera and MobileNet-SSD algorithm is proposed and applied to robot safety protection. The result shows that the image information collected by the depth camera can detect the human position in real time, which can replace the existing mechanical safety human position detection device. At the same time, the depth camera can detect only human but not mobile devices and realize the separation and early warning of people and mobile devices.

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High-speed moving target tracking of multi-camera system with overlapped field of view

Cited by 4 publications

References 24 publications

Object Tracking Using Computer Vision: A Review

Object Tracking Using Computer Vision: A Review

Sea-Surface Target Visual Tracking with a Multi-Camera Cooperation Approach

Human Position Detection Based on Depth Camera Image Information in Mechanical Safety

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