Tracking Interacting Objects Optimally Using Integer Programming

Wang, Xinchao; Türetken, Engin; Fleuret, François; Fua, Pascal

doi:10.1007/978-3-319-10590-1_2

Cited by 77 publications

(58 citation statements)

References 27 publications

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“…One way to perform tracking-by-detection is to reason in the full spatio-temporal grid formed by stacking up all possible spatial locations over time [39], [40], [41], [42], [43], [44]. This can be done optimally in polynomial time for non-dividing objects such as pedestrians and has made this approach competitive despite the large size of the graphs involved.…”

Section: Tracking By Detectionmentioning

confidence: 99%

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

Türetken

Wang

Becker

et al. 2017

IEEE Trans. Med. Imaging

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Abstract-We propose a novel approach to automatically tracking elliptical cell populations in time-lapse image sequences. Given an initial segmentation, we account for partial occlusions and overlaps by generating an over-complete set of competing detection hypotheses. To this end, we fit ellipses to portions of the initial regions and build a hierarchy of ellipses, which are then treated as cell candidates. We then select temporally consistent ones by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to partial occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.

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Section: Tracking By Detectionmentioning

confidence: 99%

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

Türetken

Wang

Becker

et al. 2017

IEEE Trans. Med. Imaging

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“…In our implementation we have relied on the KSP tracker of [12], which outputs ground-plane trajectories. The KSP tracker has been shown to achieve the stateof-the-art tracking performance, and it has been recently extended to people re-identification [11,54], tracking interaction objects [55,56] and tracking cells [51] in biomedical imagery. However, our tracker may take input for any multiobject tracker but not limited to KSP.…”

Section: Related Workmentioning

confidence: 99%

Parsing human skeletons in an operating room

Belagiannis

Wang

Shitrit

et al. 2016

Machine Vision and Applications

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Multiple human pose estimation is an important yet challenging problem. In an Operating Room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localising and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures (3DPS) and 2D body part localization across all camera views, using Convolutional Neural Networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.

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“…The optimal association of the trajectories is done using an algorithm that minimizes the network flow-cost. Wang et al 19 also map the multitarget tracking problem to a network-flow. They solve the MAP problem using a global optimization framework that uses mixed integer programming.…”

Section: Related Workmentioning

confidence: 99%

Three hypothesis algorithm with occlusion reasoning for multiple people tracking

Reta

Altamirano

González

et al. 2015

J. Electron. Imaging

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Abstract. This work proposes a detection-based tracking algorithm able to locate and keep the identity of multiple people, who may be occluded, in uncontrolled stationary environments. Our algorithm builds a tracking graph that models spatio-temporal relationships among attributes of interacting people to predict and resolve partial and total occlusions. When a total occlusion occurs, the algorithm generates various hypotheses about the location of the occluded person considering three cases: (a) the person keeps the same direction and speed, (b) the person follows the direction and speed of the occluder, and (c) the person remains motionless during occlusion. By analyzing the graph, our algorithm can detect trajectories produced by false alarms and estimate the location of missing or occluded people. Our algorithm performs acceptably under complex conditions, such as partial visibility of individuals getting inside or outside the scene, continuous interactions and occlusions among people, wrong or missing information on the detection of persons, as well as variation of the person's appearance due to illumination changes and background-clutter distracters. Our algorithm was evaluated on test sequences in the field of intelligent surveillance achieving an overall precision of 93%. Results show that our tracking algorithm outperforms even trajectory-based state-of-the-art algorithms.

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Tracking Interacting Objects Optimally Using Integer Programming

Cited by 77 publications

References 27 publications

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

Parsing human skeletons in an operating room

Three hypothesis algorithm with occlusion reasoning for multiple people tracking

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