Tracking from multiple view points: Self-calibration of space and time

Stein, G.P.

doi:10.1109/cvpr.1999.786987

Cited by 108 publications

(95 citation statements)

References 13 publications

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“…Alternatively, Stein et al [16] presented a new approach that does not require camera calibration. The camera calibration information is estimated by observing motion trajectories in the scene.…”

Section: Tracking and Handoff Between Multiple Camerasmentioning

confidence: 99%

Recognizing people in non-intersecting camera views

Bouchrika

Carter

Nixon

2009

3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009)

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Many studies have now confirmed that it is possible to recognize people by the way they walk. As yet there has been little formal study of identity tracking using gait over different camera views. We present a new approach for people tracking and identification between different non-intersecting uncalibrated cameras based on gait analysis. An identification signature is derived from gait kinematics as well as anthropometric knowledge. Given the nature of surveillance data, we have developed a new feature extraction technique for finding human legs. The novelty of our approach is motivated by the latest research for people identification using gait. The experimental results confirm the robustness of our method to extract gait features in different scenarios. Furthermore, experimental results revealed the potential of our method to work in real surveillance systems to recognize walking people over different views with achieved cross-camera recognition rates of 95% and 90% for two different views.

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Section: Tracking and Handoff Between Multiple Camerasmentioning

confidence: 99%

Recognizing people in non-intersecting camera views

Bouchrika

Carter

Nixon

2009

3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009)

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“…A number of researchers have looked at the problem of self-configuring a multi-sensor network through the exploitation of motion in the environment [3][4][5][6]. These efforts generally assume vision-based sensors and focus on issues regarding the processing of observations collected from distributed sensors.…”

Section: Related Workmentioning

confidence: 99%

“…Stein [3], for example, considered recovering a rough planar alignment of the location and orientation of the individual cameras. Using a least-median-of-squares technique, he determined the correspondence between moving objects in pairs of cameras.…”

Section: Related Workmentioning

confidence: 99%

Self-calibration of a vision-based sensor network

Marinakis

Dudek

2009

Image and Vision Computing

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When a network of vision-based sensors is emplaced in an environment for applications such as surveillance or monitoring the spatial relationships between the sensing units must be inferred or computed for self-calibration purposes. In this paper we describe a technique to solve one aspect of this self-calibration problem: automatically determining the topology and connectivity information of a network of cameras based on a statistical analysis of observed motion in the environment. While the technique can use labels from reliable cameras systems, the algorithm is powerful enough to function using ambiguous tracking data. The method requires no prior knowledge of the relative locations of the cameras and operates under very weak environmental assumptions. Our approach stochastically samples plausible agent trajectories based on a delay model that allows for transitions to and from sources and sinks in the environment. The technique demonstrates considerable robustness both to sensor error and non-trivial patterns of agent motion. The output of the method is a Markov model describing the behavior of agents in the system and the underlying traffic patterns. The concept is demonstrated with simulation data for systems containing up to 10 agents and verified with experiments conducted on a six camera sensor network.

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“…Using a temporal alignment procedure similar to that used by Stein [13], it creates a list of all possible detection pairs (one from each camera) whose timestamps differ by less than a small time period, typically less than the slowest frame interval on the cameras.…”

Section: Calibrating Spatial Associationsmentioning

confidence: 99%