A Cuboid Detection and Tracking System using A Multi RGBD Camera Setup for Intelligent Manipulation and Logistics

Hu, Haohao; Immel, Fabian; Janosovits, Johannes; Lauer, Martin; Stiller, Christoph

doi:10.1109/case49439.2021.9551449

Cited by 6 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3D Object Detection: Plane segmentation is used for detecting parcels [Hu+21] and pallets [Xia+17; MF18]. Moreover, clustering approaches [FZL21] and deep learningbased pointcloud classification [BBS21] have been used.…”

Section: Methodological Categorizationmentioning

confidence: 99%

“…2.2.3. Hu et al [Hu+21] address the problem of tracking parcels inside a moving truck. A multi-RGB-D camera setup is used to overcome the limited field of view and occlusions of a single camera setup.…”

Section: Tracking and Tracingmentioning

confidence: 99%

“…Summary: Tracking and tracing has been investigated for parcels [Wei+10; NZO18; Cla+19; Hu+21], and pallets [Bor+14; Rut+21; RU22; Klü+22]. Approaches are mostly vision-based, partially rely on RGB-D imagery [Hu+21] and leverage literature from person reidentification [RU22]. Moreover, whole systems for automating such processes [Bor+14; Rut+22] and the monitoring of logistics road traffic [BTdL19] have been investigated.…”

Section: Tracking and Tracingmentioning

confidence: 99%

See 2 more Smart Citations

Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

Naumann

Dörr

Salscheider

et al. 2020

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)

View full text Add to dashboard Cite

Computer vision applications in transportation logisticsand warehousing have a huge potential for process automation. We present a structured literature review on research in the field to help leverage this potential. All literature is categorized w.r.t. the application, i.e. the task it tackles and w.r.t. the computer vision techniques that are used. Regarding applications, we subdivide the literature in two areas: Monitoring, i.e. observing and retrieving relevant information from the environment, and manipulation, where approaches are used to analyze and interact with the environment. In addition to that, we point out directions for future research and link to recent developments in computer vision that are suitable for application in logistics. Finally, we present an overview of existing datasets and industrial solutions. We conclude that while already many research areas have been investigated, there is still huge potential for future research. The results of our analysis are also available online at https://a-nau.github.io/cv-in-logistics.1 Verband der Automobilindustrie e.V. (VDA), see https://www.vda.de/en.

show abstract

Section: Methodological Categorizationmentioning

confidence: 99%

Section: Tracking and Tracingmentioning

confidence: 99%

Section: Tracking and Tracingmentioning

confidence: 99%

See 1 more Smart Citation

Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

Naumann

Dörr

Salscheider

et al. 2020

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)

View full text Add to dashboard Cite

show abstract

“…2. To obtain an updated object map in a dynamic environment with continuous repositioning and removing boxes from the working area, especially when using pose estimation techniques based on a single image [8][9][10] or techniques that do not remove objects that have exited the scene [11,12]. 3.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the use of box size priors for 6D plane segment tracking from point clouds with applications in cargo packing

Muñoz,

Nope-Rodríguez,

Loaiza-Correa

et al. 2024

Preprint

View full text Add to dashboard Cite

Available solutions to assist human operators in cargo packing processes offer alternatives to maximize the spatial occupancy of containers used in intralogistics. However, these solutions consist of sequential instructions for picking each box and positioning it in the containers, making it challenging for an operator to interpret and requiring them to alternate between reading the instructions and executing the task. A potential solution to these issues lies in a tool that naturally communicates each box's initial and final location in the desired sequence to the operator. While 6D visual object tracking systems have demonstrated good performance, they have yet to be evaluated in real-world scenarios of manual box packing. They also need to use the available prior knowledge of the packing operation, such as the number of boxes, box size, and physical packing sequence. This study explores the inclusion of box size priors in 6D plane segment tracking systems driven by images from moving cameras and quantifies their contribution in terms of tracker performance when assessed in manual box packing operations. To do this, it compares the performance of a plane segment tracking system, considering variations in the tracking algorithm and camera speed (onboard the packing operator) during the mapping of a manual cargo packing process. The tracking algorithm varies at two levels: algorithm (Awpk), which integrates prior knowledge of box sizes in the scene, and algorithm (Awoutpk), which assumes ignorance of box properties. Camera speed is also evaluated at two levels: low speed (Slow) and high speed (Shigh). This study analyzes the impact of these factors on the precision, recall, and F1-score of the plane segment tracking system. ANOVA analysis was applied to the precision and F1-score results, which allows determining that neither the camera speed-algorithm interactions nor the camera speed are significant in the precision of the tracking system. The factor that presented a significant effect is the tracking algorithm. Tukey's pairwise comparisons concluded that the precision and F1-score of each algorithm level are significantly different, with algorithm Awpk being superior in each evaluation. This superiority reaches its maximum in the tracking of top plane segments: 22 and 14 percentage units for precision and F1-score metrics, respectively. However, the results on the recall metric remain similar with and without the addition of prior knowledge. The contribution of including prior knowledge of box sizes in (6D) plane segment tracking algorithms is identified in reducing false positives. This reduction is associated with significant increases in the tracking system's precision and F1-score metrics. Future work will investigate whether the identified benefits propagate to the tracking problem on objects composed of plane segments, such as cubes or boxes.

show abstract

TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains

Naumann,

Hertlein,

Dörr

et al. 2024

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

View full text Add to dashboard Cite

A Cuboid Detection and Tracking System using A Multi RGBD Camera Setup for Intelligent Manipulation and Logistics

Cited by 6 publications

References 16 publications

Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection

Evaluation of the use of box size priors for 6D plane segment tracking from point clouds with applications in cargo packing

TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains

Contact Info

Product

Resources

About