Robust Multi-Pedestrian Tracking in Thermal-Visible Surveillance Videos

Leykin, Alex; Hammoud, Riad I.

doi:10.1109/cvprw.2006.175

Cited by 41 publications

(27 citation statements)

References 20 publications

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“…Statistical variables are used in the foreground detection to classify the pixels as foreground or background. Recent SBM use Generalized Gaussian Mixture Modeling [1], Bayesian approaches [20,21], Support Vector Regression learning approaches [27] or Codebook [5,11,14].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy foreground detection for infrared videos

Baf

Bouwmans

Vachon

2008

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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We present a foreground detection algorithm based on a fuzzy integral that is particularly suitable for infrared videos. The proposed detection of moving objects is based on fusing intensity and textures using fuzzy integral. The detection results are then used to update the background in a fuzzy way. This method allows to robustly detect moving object in presence of cloudy and rainy conditions. Our theoretical and experimental results show that the proposed method gives similar results than the KaewTraKulPong and Bowden approach based on Mixture Of Gaussians (MOG) with less memory requirement and time consuming. The results using the OTCBVS benchmark/test dataset videos show the robustness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Fuzzy foreground detection for infrared videos

Baf

Bouwmans

Vachon

2008

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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“…Therefore, systems based on the assumption that pedestrians are hotter than the environment usually produce partly or complete misdetections. In order to cope with these deficiencies, some pedestrian detection [17], [6] and tracking [52] techniques have been proposed which are based on contemporary use of a visible and an infrared system in order to utilize the benefits of both approaches. Although, only a few research efforts have been carried out on this subject, the result of visible and infrared fusion has been relatively promising.…”

Section: Pedestrian Detection/trackingmentioning

confidence: 99%

“…Some examples include military applications such as target acquisition [20], autonomous vehicle navigation [66], collision avoidance [88], terrain analysis [69], etc. ; and surveillance applications like pedestrian detection/tracking [4], [52], face detection/recognition [43], [77], etc.…”

Section: Goals and Motivationmentioning

confidence: 99%

Structure from Infrared Stereo Images

Hajebi

Zelek

2008

2008 Canadian Conference on Computer and Robot Vision

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“…These methods normally require an accurate calibration of both cameras and microphones, a solution in most cases impractical if not using a particular and a priori known displacement of the sensors. These are just a few examples, however there exists multi-modal algorithms that use inputs of video together with thermic images [16], RFID [7,11], etc. to obtain a more robust task-driven perfomance.…”

Section: Introductionmentioning

confidence: 99%

A Closed Form Solution for the Self-Calibration of Heterogeneous Sensors

Crocco

Barbosa

Murino

2012

Procedings of the British Machine Vision Conference 2012

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We present a novel closed-form solution for the joint self-calibration of video and range sensors. The approach single assumption is the availability of synchronous time of flight (i.e., range distances) measurements and visual position of the target on images acquired by a set of cameras. In such case, we make explicit a rank constraint that is valid for both image and range data. This rank property is used to find an initial and affine solution via bilinear factorization, which is then corrected by enforcing the metric constraints characteristic for both sensor modalities (i.e., camera and anchors constraints). The output of the algorithm is the identification of the target/range sensor position and the calibration of the cameras. The application extent of our approach is broad and versatile. In fact, with the same framework, we can deal with, but not restricted to, two very different applications. The first is aimed at calibrating cameras and microphones deployed in an unknown environment. The second uses a RGB-D device to reconstruct the 3D position of a set of keypoints using the camera and depth map images. Synthetic and real tests show the algorithm performance under different levels of noise and configurations of target locations, number of sensors and cameras.

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Robust Multi-Pedestrian Tracking in Thermal-Visible Surveillance Videos

Cited by 41 publications

References 20 publications

Fuzzy foreground detection for infrared videos

Fuzzy foreground detection for infrared videos

Structure from Infrared Stereo Images

A Closed Form Solution for the Self-Calibration of Heterogeneous Sensors

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