Mono-Vision based Moving Object Detection using Semantic-Guided RANSAC

Chen, Songming; Sun, Haixin; Frémont, Vincent

doi:10.1109/mfi52462.2021.9591165

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Nonetheless, the aforementioned methods only leverage geometric information for the object clustering, which often fails in complex scenes. A novel Semantic-Guided RANSAC approach is thus presented in [7] for moving objects segmentation in heavy traffic scenarios. The semantic constraint provides potential moving objects prior and the geometric epipolar residuals are used for the final moving objects verification, which exhibits promising results.…”

Section: A Vision-based Methodsmentioning

confidence: 99%

A Semantic-Guided LiDAR-Vision Fusion Approach for Moving Objects Segmentation and State Estimation

Chen

Sun

Frémont

2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Moving Objects Segmentation (MOS) is critical and indispensable for secure intelligent vehicle operation in the dynamic environment. For the state estimation task which is based on the assumption of static surroundings, to identify and filter out the moving objects plays an important role in robust ego-motion estimation. In this paper, a LiDAR-Vision fusion approach is developed to segment moving objects in the scene, which utilizes the LiDAR-based semantic segmentation as a prior and vision-based geometric information for validation. The effectiveness of our approach to segment moving objects is highlighted by the comparison with the traditional robust kernel-based outlier rejection methods. Our approach is benchmarked with three city category sequences in the KITTI dataset, which outperforms the kernel-based methods and achieves the leading results of 77.9% average fitness and 7.65 cm RMSE respectively.

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Section: A Vision-based Methodsmentioning

confidence: 99%

A Semantic-Guided LiDAR-Vision Fusion Approach for Moving Objects Segmentation and State Estimation

Chen

Sun

Frémont

2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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“…Modern vehicles are equipped with various sensors to fully perceive the surrounding environment so the system can be more robust. Data fusion provides improved performances in various tasks such as [10], [21], [25], [26], so it is worth developing the fusion model between different sensors.…”

Section: Related Workmentioning

confidence: 99%

EV-FuseMODNet: Moving Object Detection Using the Fusion of Event Camera and Frame Camera

Sun,

Chen,

Dao

et al. 2023

2023 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

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Moving object detection is a crucial task for autonomous vehicles. Indeed, dynamic objects represent higher collision risk than static ones, so the trajectories of the vehicles must be planned according to the motion forecasting of the moving participants of the scene. For the traditional framebased cameras, images can provide accumulated pixel brightness without temporal information between them. The optical flow computation is used as the inter-frame motion information. Interestingly, event-based camera can preserve the motion information by delivering the precise timestamp of each asynchronous event data, which is more suitable for the motion analysis. Also, the event-based cameras' high temporal resolution and high dynamic range allow them to work in fast motion and extreme light scenarios. In this work, we propose a new Deep Neural Network, called EV-FuseMODNet for Moving Object Detection (MOD) that captures motion and appearance information from both event-based and frame-based cameras. The proposed method has been evaluated with the extended KittiMoSeg dataset and the generated dark KITTI sequence. An overall 27.5% relative improvement on the extended KittiMoSeg dataset compared to the state-of-the-art approaches has been achieved. The code is released in https://github.com/adosum/EV-FuseMODNet.

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Mono-Vision based Moving Object Detection using Semantic-Guided RANSAC

Cited by 2 publications

References 15 publications

A Semantic-Guided LiDAR-Vision Fusion Approach for Moving Objects Segmentation and State Estimation

A Semantic-Guided LiDAR-Vision Fusion Approach for Moving Objects Segmentation and State Estimation

EV-FuseMODNet: Moving Object Detection Using the Fusion of Event Camera and Frame Camera

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