Multi-Commodity Network Flow for Tracking Multiple People

Machine Vision and Applications

Shitrit

et al. 2016

Self Cite

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.

Section: Related Workmentioning

confidence: 99%

Parsing human skeletons in an operating room

Belagiannis

Machine Vision and Applications

Shitrit

et al. 2016

Self Cite

“…They rely on Conditional Random Fields [17,27], belief Propagation [29,9], Dynamic or Linear Programming [3,10]. Among the latter, some operate on graphs whose nodes can either be all the spatial locations of potential people presence [26,11,5,1], only those where a detector has fired [25,15], or short temporal sequences of consecutive detections that are very likely to correspond to the same person [21,30,23,4].…”

Section: Related Workmentioning

confidence: 99%

Tracking Interacting Objects Optimally Using Integer Programming

Computer Vision – ECCV 2014

Türetken

Fleuret

et al. 2014

Self Cite

Abstract. In this paper, we show that tracking different kinds of interacting objects can be formulated as a network-flow Mixed Integer Program. This is made possible by tracking all objects simultaneously and expressing the fact that one object can appear or disappear at locations where another is in terms of linear flow constraints. We demonstrate the power of our approach on scenes involving cars and pedestrians, bags being carried and dropped by people, and balls being passed from one player to the next in a basketball game. In particular, we show that by estimating jointly and globally the trajectories of different types of objects, the presence of the ones which were not initially detected based solely on image evidence can be inferred from the detections of the others.

“…Player tracking To track the players on the ground plane, we apply the Multi-Commodity Network Flow proposed in [28], which relies on the publicly-available software [4,5]. We obtain people tracks with team memberships from the two teams and the referees.…”

Section: Game Phase and Player Trajectoriesmentioning

confidence: 99%

Take your eyes off the ball: Improving ball-tracking by focusing on team play

Computer Vision and Image Understanding

Ablavsky

Shitrit

et al. 2014

Self Cite

Abstract. Accurate video-based ball tracking in team sports is important for automated game analysis, and has proven very difficult because the ball is often occluded by the players. In this paper, we propose a novel approach to addressing this issue by formulating the tracking in terms of deciding which player, if any, M is in possession of the ball at any given time. This is very different from standard approaches that first attempt to track the ball and only then to assign possession. We will show that our method substantially increases performance when applied to long basketball and soccer sequences.