Optic flow from unstable sequences through local velocity constancy maximization

Pauwels, Karl; Hulle, Marc M. Van

doi:10.1016/j.imavis.2008.04.010

Cited by 23 publications

(17 citation statements)

References 22 publications

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“…The optical flow algorithm integrates the temporal phase gradient across different orientations and also uses a coarse-to-fine scheme to increase the dynamic range [37]. Unlike [37], we estimate the temporal phase gradient using two instead of five frames, more specifically the frames I t and I t+1 , at times t and t + 1.…”

Section: ) Dense Motion Cuesmentioning

confidence: 99%

Real-Time Pose Detection and Tracking of Hundreds of Objects

Pauwels

Rubio

Ros

2016

IEEE Trans. Circuits Syst. Video Technol.

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PostprintThis is the accepted version of a paper published in IEEE transactions on circuits and systems for video technology (Print). This paper has been peer-reviewed but does not include the final publisher proofcorrections or journal pagination.Citation for the original published paper (version of record): Ros, E. (2015) Real-time Pose Detection and Tracking of Hundreds of Objects. IEEE transactions on circuits and systems for video technology (Print)Access to the published version may require subscription. Abstract-We propose a novel model-based method for tracking the six-degrees-of-freedom (6DOF) pose of a very large number of rigid objects in real-time. By combining dense motion and depth cues with sparse keypoint correspondences, and by feeding back information from the modeled scene to the cue extraction process, the method is both highly accurate and robust to noise and occlusions. A tight integration of the graphical and computational capability of graphics processing units (GPUs) allows the method to simultaneously track hundreds of objects in real-time. We achieve pose updates at framerates around 40 Hz when using 500,000 data samples to track 150 objects using images of resolution 640×480. We introduce a synthetic benchmark dataset with varying objects, background motion, noise and occlusions that enables the evaluation of stereo-visionbased pose estimators in complex scenarios. Using this dataset and a novel evaluation methodology, we show that the proposed method greatly outperforms state-of-the-art methods. Finally, we demonstrate excellent performance on challenging real-world sequences involving multiple objects being manipulated.

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Section: ) Dense Motion Cuesmentioning

confidence: 99%

Real-Time Pose Detection and Tracking of Hundreds of Objects

Pauwels

Rubio

Ros

2016

IEEE Trans. Circuits Syst. Video Technol.

Self Cite

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“…Over the years, a lot of research has been carried out in the field of optical flow algorithms and the latter have been continuously improved, sometimes by concentrating on the algorithm itself [19,[29][30][31], sometimes by combining two of them [32,33], and sometimes by combining with other techniques [4,16,34]. Although most optical flow algorithms were designed with the main objective of obtaining accurate results, the trade-offs between efficiency and accuracy in optical flow algorithms are highlighted in [35] as well as the importance of an efficient optical flow computation in many real-world applications.…”

Section: Parallelization Of the Optical Flowmentioning

confidence: 99%

“…or the motion of objects within the scene are restricted [13][14][15]. Except for some recent articles normally associated with the movement of conventional vehicles [16,17], mobile robots [18,19], and handheld cameras [20,21], the majority of papers describe systems in which the variation of the scene is limited. This limitation is determined because they work with images with a static background and taken with a static camera.…”

Section: Introductionmentioning

confidence: 99%

“…The system is applied to an environment which is particularly hostile in terms of optical flow computation and has rarely been described by other authors [19]. It aims to determine the motion visually perceived by the driver of a conventional vehicle through roads or streets under real conditions: with real traffic and moving at speeds ranging from a few kilometers per hour to 120 km/h.…”

Section: Introductionmentioning

confidence: 99%

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Parallelization of the optical flow computation in sequences from moving cameras

et al. 2014

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This paper presents a flexible and scalable approach to the parallelization of the computation of optical flow. This approach is based on data parallel distribution. Images are divided into several subimages processed by a software pipeline while respecting dependencies between computation stages. The parallelization has been implemented in three different infrastructures: shared, distributed memory, and hybrid to show its conceptual flexibility and scalability. A significant improvement in performance was obtained in all three cases. These versions have been used to compute the optical flow of video sequences taken in adverse conditions, with a moving camera and natural-light conditions, on board a conventional vehicle traveling on public roads. The parallelization adopted has been developed from the analysis of dependencies presented by the well-known Lucas-Kanade algorithm, using a sequential version developed at the University of Porto as the starting point.

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“…We use phase-based techniques for the optical flow (Pauwels and Van Hulle, 2009) and disparity (Sabatini et al, 2007) estimation, and a continuous-time algorithm for the egomotion estimation (Pauwels and Van Hulle, 2006). The latter operates on the monocular optical flow field.…”

Section: Visual Cue Combinationmentioning

confidence: 99%

A Three-Level Architecture for Model–free Detection and Tracking of Independently Moving Objects

Pugeault

Pauwels

Hulle

et al. 2010

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:We present a three-level architecture for detection and tracking of independently moving objects (IMOs) in sequences recorded from a moving vehicle. At the first stage, image pixels with an optical flow that is not entirely induced by the car's motion are detected by combining dense optical flow, egomotion extracted from this optical flow, and dense stereo. These pixels are segmented and an attention mechanism is used to process them at finer resolution at the second level making use of sparse 2D and 3D edge descriptors. Based on the rich and precise information on the second level, the full rigid motion for the environment and for each IMO is computed. This motion information is then used for tracking, filtering and the building of a 3D model of the street structure as well as the IMO. This multi-level architecture allows us to combine the strength of both dense and sparse processing methods in terms of precision and computational complexity, and to dedicate more processing capacity to the important parts of the scene (the IMOs).

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Optic flow from unstable sequences through local velocity constancy maximization

Cited by 23 publications

References 22 publications

Real-Time Pose Detection and Tracking of Hundreds of Objects

Real-Time Pose Detection and Tracking of Hundreds of Objects

Parallelization of the optical flow computation in sequences from moving cameras

A Three-Level Architecture for Model–free Detection and Tracking of Independently Moving Objects

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