computer-vision

Moving Object Detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in moving-object-detection

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Dataset

Best Model

Actions

DVSMOTION20

Libraries

i

Use these libraries to find moving-object-detection models and implementations

Datasets

RF100

DVSMOTION20

Subtasks

No subtasks available.

Most implemented papers

RGB-Event Fusion for Moving Object Detection in Autonomous Driving

F. Yang, C. Demonceaux, Zhuyun Zhou, Zongwei Wu, Rémi Boutteau, D. Ginhac•Fri Sep 16 2022

RENet is a novel RGB-Event fusion Network that jointly exploits the two complementary modalities to achieve more robust MOD under challenging scenarios for autonomous driving and performs significantly better than the state-of-the-art RGB- event fusion alternatives.

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Unsupervised Moving Object Detection via Contextual Information Separation

Stefano Soatto, D. Scaramuzza, Antonio Loquercio, Yanchao Yang•Wed Jan 09 2019

An adversarial contextual model for detecting moving objects in images that can be thought of as a generalization of classical variational generative region-based segmentation, but in a way that avoids explicit regularization or solution of partial differential equations at run-time.

140 0

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UAV Images Dataset for Moving Object Detection from Moving Cameras

Ibrahim Delibasoglu•Sat Mar 20 2021

This paper presents a new high resolution aerial images dataset in which moving objects are labelled manually in order to contribute to the evaluation of the moving object detection methods for moving cameras.

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Moving Object Detection for Event-based Vision using k-means Clustering

Anindya Mondal, M. Das•Fri Sep 03 2021

This work uses k-Means clustering for detecting moving objects in event-based data and compares the proposed method against state-of-the-art algorithms and shows performance improvement over them.

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Moving Object Detection for Event-based Vision using Graph Spectral Clustering

T. Bouwmans, Anindya Mondal, R. Shashant, Jhony H. Giraldo, A. Chowdhury•Wed Sep 29 2021

An unsupervised Graph Spectral Clustering technique for Moving Object Detection in Event-based data (GSCEventMOD) is presented and it is shown how the optimum number of moving objects can be automatically determined.

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FMODetect: Robust Detection of Fast Moving Objects

Jiri Matas, M. Pollefeys, D. Rozumnyi, F. Šroubek, Martin R. Oswald•Mon Dec 14 2020

A matting and fitting network that estimates the blurred appearance without background, followed by an energy minimization based deblurring allows achieving real-time performance, i.e. an order of magnitude speed-up, and thus enabling new classes of application.

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Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark

Qingyong Hu, Feng Zhang, Yingqian Wang, Zaiping Lin, Yulan Guo, Qian Yin, Hao Liu, Wei An•Wed Nov 24 2021

A large-scale satellite video dataset with rich annotations for the task of moving object detection and tracking is built and a motion modeling baseline is introduced to improve the detection rate and reduce false alarms.

118 0

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RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud

Chris Xiaoxuan Lu, Fangqiang Ding, Zhijun Pan, Hantao Zhong•Sun Sep 17 2023

This work introduces RaTrack, an innovative solution tailored for radar-based tracking, which focuses on motion segmentation and clustering, enriched by a motion estimation module, and showcases superior tracking precision of moving objects.

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Towards Segmenting Anything That Moves

Deva Ramanan, Achal Dave, P. Tokmakov•Sun Feb 10 2019

This work proposes a simple learning-based approach for spatio-temporal grouping that leverages motion cues from optical flow as a bottom-up signal for separating objects from each other, and shows that this model matches top-down methods on common categories, while significantly out-performing both top- down and bottom- up methods on never-before-seen categories.

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HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery

Hakki Motorcu, H. F. Ates, H. F. Ugurdag, B. Gunturk•Mon Oct 18 2021

HM-Net outperforms state-of-the-art WAMI moving object detection and tracking methods on WPAFB dataset with its 96.2% F1 and 94.4% mAP detection scores, while achieving 61.8 % mAP tracking score on the same dataset.

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