Tracking objects from multiple and moving cameras

Kang, Jung Julie; Cohen, Isaac; Medioni, Gérard

doi:10.1049/ic:20040094

Cited by 9 publications

(5 citation statements)

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“…Camera motions, including panning, tilt, and zooming, make the video frame coordinates variable with respect to the coordinate system, which is the hockey rink in this case. Multiple cameras are able to solve the problem of localizing targets in the real world coordinate system [KCM04] so long as the relative location and configuration of different cameras are known and well synchronized. However, because the source data we use for this task is extracted from only one monocular TV broadcasting camera, it is very difficult to derive even the relative positions among the targets in the real world.…”

Section: Target Localizationmentioning

confidence: 99%

Robust Visual Tracking for Multiple Targets

Cai

Freitas

Little

2006

Computer Vision – ECCV 2006

145

110

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We address the problem of robust multi-target tracking within the application of hockey player tracking. Although there has been extensive work in multi-target tracking, there is no existing visual tracking system that can automatically and robustly track a variable number of targets and correctly maintain their identities with a monocular camera regardless of background clutter, camera motion and frequent mutual occlusion between targets. We build our system on the basis of the previous work by Okuma et al. [OTdF + 04]. The particle filter technique is adopted and modified to fit into the multi-target tracking framework. A rectification technique is employed to map the locations of players from the video frame coordinate system to the standard hockey rink coordinates so that the system can compensate for camera motion and the dynamics of players on the rink can be improved by a second order auto-regression model. A global nearest neighbor data association algorithm is introduced to assign boosting detections to the existing tracks for the proposal distribution in particle filters. The mean-shift algorithm is embedded into the particle filter framework to stabilize the trajectories of the targets for robust tracking during mutual occlusion. The color model of the targets is also improved by the kernel introduced by mean-shift. Experimental results show that our system is able to correctly track all the targets in the scene even if they are partially or completely occluded for a period of time.

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Section: Target Localizationmentioning

confidence: 99%

Robust Visual Tracking for Multiple Targets

Cai

Freitas

Little

2006

Computer Vision – ECCV 2006

145

110

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“…Optical Flow algorithms are used to track objects in static and dynamic background and are robust to noise [8] [9] [10] [11].Optical Flow method is implemented, through MACH filter, log r-θ mapping to track an object [6] and also polar-log images is proposed as an effective method to track multiple objects [7]. Colour and blob tracking been done through Bayesian tracking methods [12] and also by using multiple stationary and moving cameras [13].…”

Section: Introductionmentioning

confidence: 99%

Optical Flow Based Multiple Object Detection and Tracking System in Static and Dynamic Environment

Gayathri.D¹,

Dattatreya²

2014

IOSRJECE

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“…It may eliminate the ball or may cut apart the players' feet making small sections which look similar to the ball. Kalman and particle filters have usually been used for tracking players [4,6,7,9,15]. But tracking with these filters depends on specific parameters and cannot handle topological changes [16].…”

Section: Introductionmentioning

confidence: 99%

Effective tracking of the players and ball in indoor soccer games in the presence of occlusion

Kasiri-Bidhendi

Safabakhsh

2009

2009 14th International CSI Computer Conference

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Object tracking is one of the major subjects in machine vision and plays a main role in detection of major events in indoor soccer matches. In this paper, a novel approach for tracking the ball and players is proposed. In this method, the ground lines are segmented and eliminated using a fast and effective method. Then, the remaining non-field pixels are considered and labeled as players and the ball. A fast level set contour is used to track players and the ball. The proposed method can track players and the ball in presence of occlusion. Experiments show that the proposed method is robust to occlusion and different field colors.

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Tracking objects from multiple and moving cameras

Cited by 9 publications

References 0 publications

Robust Visual Tracking for Multiple Targets

Robust Visual Tracking for Multiple Targets

Optical Flow Based Multiple Object Detection and Tracking System in Static and Dynamic Environment

Effective tracking of the players and ball in indoor soccer games in the presence of occlusion

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