Active learning of driving scenario trajectories

Jarl, Sanna; Aronsson, Linus; Rahrovani, Sadegh; Chehreghani, Morteza Haghir

doi:10.1016/j.engappai.2022.104972

Cited by 11 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The random sampling strategy is mostly used for comparison purposes, grounded on to the principle that AL-based methods have the potential to perform better (e.g., [5,48,52,60]). This method uses a random function with uniform distribution over the interval [0, 1], and it consists in selecting an image from the pool uniformly random.…”

Section: Random Methodsmentioning

confidence: 99%

“…Military applications, such as aircraft detection on very high-resolution imagery, are using AL too [6], to tackle the laborious nature of the tasks involved in the annotation of WSI. In the field of autonomous driving, AL is used to annotate trajectory time-series data and discover new unknown classes [52], to locate 3D objects from 2D image detector [53], to estimate sample uncertainties [54] or diversity [16] for 2D or 3D scenarios, to perform video OD in road scenes [9], and to label portion of the dataset on a device-federated learning-avoiding the transmission of the information [34] through communication networks, among other purposes. Also, other applications benefit from AL, such as defect detection [55] and virtual reality-based pose estimation [7].…”

Section: Active Learning In Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

Ten Years of Active Learning Techniques and Object Detection: A Systematic Review

Garcia,

Carias,

Adão

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Object detection (OD) coupled with active learning (AL) has emerged as a powerful synergy in the field of computer vision, harnessing the capabilities of machine learning (ML) to automatically identify and perform image-based objects localisation while actively engaging human expertise to iteratively enhance model performance and foster machine-based knowledge expansion. Their prior success, demonstrated in a wide range of fields (e.g., industry and medicine), motivated this work, in which a comprehensive and systematic review of OD and AL techniques was carried out, considering reputed technical/scientific publication databases—such as ScienceDirect, IEEE, PubMed, and arXiv—and a temporal range between 2010 and December 2022. The primary inclusion criterion for papers in this review was the application of AL techniques for OD tasks, regardless of the field of application. A total of 852 articles were analysed, and 60 articles were included after full screening. Among the remaining ones, relevant topics such as AL sampling strategies used for OD tasks and groups categorisation can be found, along with details regarding the deep neural network architectures employed, application domains, and approaches used to blend learning techniques with those sampling strategies. Furthermore, an analysis of the geographical distribution of OD researchers across the globe and their affiliated organisations was conducted, providing a comprehensive overview of the research landscape in this field. Finally, promising research opportunities to enhance the AL process were identified, including the development of novel sampling strategies and their integration with different learning techniques.

show abstract

Section: Random Methodsmentioning

confidence: 99%

Section: Active Learning In Deep Learningmentioning

confidence: 99%

Ten Years of Active Learning Techniques and Object Detection: A Systematic Review

Garcia,

Carias,

Adão

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…An enormous amount of research has already been done on the perception system of autonomous driving ( [16], [17], [18]), but there is still a lot to discover and update. Moreover, solving different types of perception tasks require different kinds of methods.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Point Clouds and Images for Autonomous Vehicles: Trends, Challenges, and Opportunities

Munia,

Kunjumon,

Khosravi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Nowadays, researches on motion trajectory are mainly about the identification and detection of motion behaviors [9,10,11,12,13,14,15,16,17,18,19]. Izakian et al [11] proposed a cluster-centric trajectory segmentation approach with a precision over 80%, then, from a more comprehensive perspective, detected the behavior change points of moving objects.…”

Section: Introductionmentioning

confidence: 99%

“…Khan [18] used the motion features derived from optical flow and particle advection to monitor oscillations in video motion trajectories to show crowd congestion, and the mean average precision (mAP) of the congested region was about 60% that was much higher than the baseline. Jarl et al [19] developed a general active learning framework to annotate driving scenarios of motion trajectories, which discussed the relationship between the number of queries and the F1 score in different classifiers. The above mentioned carried out general studies on motion trajectory in the field of transportation, while trajectory research applicable to agricultural scenarios should be further explored.…”

Section: Introductionmentioning

confidence: 99%

Field & Road-CENet: Semantic Segmentation for Agricultural Machinery Trajectory Segmentation

Huang

Wan

2023

Preprint

View full text Add to dashboard Cite

Agricultural machinery management is the key to agricultural production, andtrajectory segmentation lays an important foundation for machinerymanagement. For big data platform of agricultural machinery, it is hoped tosimultaneously improve both of segmentation accuracy and segmentationefficiency to satisfy the processing requirements. However, traditional machinelearning algorithms need to manually adjust parameters and do not integratemultiple features when dealing with trajectory segmentation. This paper aims atthe above problems by modifying the CE-Net model to improve the segmentationaccuracy and ensure the processing efficiency. A creative method for constructingtrajectory image from discrete trajectory data was developed in this paper. Thenew trajectory image showed both the temporal and the spatial characteristics ofthe operation. Then, a semantic segmentation algorithm, Field & Road- CENet,was put forward. The proposed algorithm modified the network CE-Net by addingtwo modules, Standard Convolution Residual Block and Global MaxpoolingAttention Mechanism, to fuse original feature information and enhance thesemantic expression of low-level feature maps. Two cotton sowing datasets werebuilt, including the meter level and the centimeter level. Experiment results showthat Field & Road-CENet performed well on both datasets. In the fieldsegmentation that is the most concerned, the identification accuracy reached97.8% and 95.2%, respectively, and the average accuracy of field and road was94.2% and 88.3%, respectively. In conclusion, this work verifies the feasibility ofusing semantic segmentation to realize trajectory segmentation of agriculturalmachinery. Compared with the current researches, the proposed method isapplicable to trajectory data with two precisions, which has stronger domaingeneralization ability. And it performs quite fast with an average inference time of 0.044 s for each image block, demonstrating that the proposed algorithm issuitable for the big data processing of agricultural machinery.

show abstract

Active learning of driving scenario trajectories

Cited by 11 publications

References 11 publications

Ten Years of Active Learning Techniques and Object Detection: A Systematic Review

Ten Years of Active Learning Techniques and Object Detection: A Systematic Review

Semantic Segmentation of Point Clouds and Images for Autonomous Vehicles: Trends, Challenges, and Opportunities

Field & Road-CENet: Semantic Segmentation for Agricultural Machinery Trajectory Segmentation

Contact Info

Product

Resources

About

Active learning of driving scenario trajectories

Cited by 11 publications

References 11 publications

Ten Years of Active Learning Techniques and Object Detection: A Systematic Review

Ten Years of Active Learning Techniques and Object Detection: A Systematic Review

Semantic Segmentation of Point Clouds and Images for Autonomous Vehicles: Trends, Challenges, and Opportunities

Field &amp; Road-CENet: Semantic Segmentation for Agricultural Machinery Trajectory Segmentation

Contact Info

Product

Resources

About

Field & Road-CENet: Semantic Segmentation for Agricultural Machinery Trajectory Segmentation