Shadow detection: A survey and comparative evaluation of recent methods

Sanin, Andres; Sanderson, Conrad; Lovell, Brian C.

doi:10.1016/j.patcog.2011.10.001

Cited by 297 publications

(239 citation statements)

References 47 publications

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“…Second, SILTP is employed to detect the shadow region in the set of moving pixels, by which a shadow region 3 S can be gained. Third, the thresholds of HSV color model are achieved by maximum likelihood estimation (MLE) and a shadow region 2 S is detected by HSV color model. Finally, the above two shadow regions we have detected are synthesized by logical operation and get a final shadow region 3 S .…”

Section: Proposed Methodsmentioning

confidence: 99%

“…A survey and a comparative evaluation of recent techniques for moving cast shadow detection are presented in [2]. The methods of cast shadow detection covered in the survey are divided into four categories according to the features they are based on: chromaticity-based methods, physical-based methods, geometry-based methods and texture-based methods.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Cast Shadow Detection with Combined Texture and Color Models

Liu¹,

Zhu²

2014

IJET

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Abstract-Cast shadows of moving foreground objects can cause miss tracking problem in object detection and tracking, thus shadow detection is an important step used after a moving foreground object is detected. Most of current methods have a significant trade-off between the shadow detection rate and the shadow discrimination rate. In this paper, an effective and adaptive method with combined texture and color models is proposed in order to achieve good shadow detection rate and shadow discrimination rate as well. Firstly, Scale Invariant Local Ternary Pattern (SILTP) is used to select a candidate shadow region. Then HSV color model is employed to detect a new candidate shadow region by using maximum likelihood estimation (MLE) to estimate the thresholds of HSV color model adaptively. Finally the two regions are combined by logical operation and a new shadow region can be obtained. Our experimental results show that the proposed method achieves a better performance in both shadow detection rate and discrimination rate compared to the other current methods. Moreover, the proposed method runs at 100 frames per second and is suitable for the real-time detection and tracking.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Cast Shadow Detection with Combined Texture and Color Models

Liu¹,

Zhu²

2014

IJET

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“…23,24 2.3 Occlusion Management Except when the camera is located above the road, with perpendicular viewing to the road surface, when vehicles are close, they partially occlude one another and correct counting is difficult. The problem becomes harder when the occlusion occurs as soon as the vehicles appear in the field of view.…”

Section: Shadow Removalmentioning

confidence: 99%

Automatic vehicle counting system for traffic monitoring

Crouzil

Khoudour

Valiere

et al. 2016

J. Electron. Imaging

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“…One is the multiple object tracking (MOT) metric proposed in [58], together with its quantities, MOT precision (MOTP) and MOT accuracy (MOTA). The MOTP measures the precision of the tracking system by comparing it with a threshold value pre-defined in terms of the Euclidean distance (either in pixels [59] or meters [58]). On the other hand, the MOTA measures the tracking configuration errors, consisting of the false positives (i.e.…”

Section: A Setupmentioning

confidence: 99%

Audio Assisted Robust Visual Tracking With Adaptive Particle Filtering

Kılıç

Barnard

Wang

et al. 2015

IEEE Trans. Multimedia

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Abstract-The problem of tracking multiple moving speakers in indoor environments has recently received much attention. Earlier techniques were based purely on vision, but the theoretical and algorithmic advances and a constant growth in speed of processing have led to the emergence of techniques which allow the fusion of audio and visual data. The fusion of multi-modal information has been shown to be instrumental in improving tracking performance, as well as robustness in the case of challenging situations like occlusions (by the limited field of view of cameras or by other speakers). However, data fusion algorithms often suffer from noise corrupting the sensor measurements which cause non-negligible detection errors. Here, a novel approach to combining audio and visual data is proposed. In our framework, we employ audio data as an aid to particle filter (PF) based visual tracking, by using the direction of arrival angles of the audio sources to reshape the typical Gaussian noise distribution of particles in the propagation step and to weight the observation model in the measurement step. This approach is further improved by solving a typical problem associated with the PF. It has been observed that the efficiency and accuracy of the PF usually depend on the number of particles and noise variance used in the estimation and propagation functions for re-allocating these particles at each iteration. Both of these parameters are specified beforehand and are kept fixed in the regular implementation of the PF which makes the tracker unstable in practice. To address these problems, we design an algorithm which adapts both the number of particles and noise variance based on tracking error and the area occupied by the particles in the image. Experiments on the AV 16.3 dataset show the advantage of our proposed methods over the baseline PF method and an existing adaptive PF algorithm for tracking occluded speakers with a significantly reduced number of particles.Index Terms-Audio-visual speaker tracking, particle filter, adaptive particle filter.

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Shadow detection: A survey and comparative evaluation of recent methods

Cited by 297 publications

References 47 publications

An Adaptive Cast Shadow Detection with Combined Texture and Color Models

An Adaptive Cast Shadow Detection with Combined Texture and Color Models

Automatic vehicle counting system for traffic monitoring

Audio Assisted Robust Visual Tracking With Adaptive Particle Filtering

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