Exploiting multiple cues in motion segmentation based on background subtraction

Huerta, Iván; Amato, Ariel; Roca, Xavier; Gonzílez, Jordi

doi:10.1016/j.neucom.2011.10.036

Cited by 21 publications

(17 citation statements)

References 53 publications

(109 reference statements)

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“…In order to achieve moving foreground segmentation, a hybrid approach presented in [48], which fuses invariant colour and gradient models, is used (please refer to [48] for further details). This approach can cope with several motion segmentation challenges, e.g., illumination changes, achromatic shadows, since it is based on a chromatic colour model [25].…”

Section: Moving Foreground Segmentationmentioning

confidence: 99%

Chromatic shadow detection and tracking for moving foreground segmentation

Huerta

Holte

Moeslund

et al. 2015

Image and Vision Computing

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Advanced segmentation techniques in the surveillance domain deal with shadows to avoid distortions when detecting moving objects. Most approaches for shadow detection are still typically restricted to penumbra shadows and cannot cope well with umbra shadows. Consequently, umbra shadow regions are usually detected as part of moving objects, thus affecting the performance of the final detection. In this paper we address the detection of both penumbra and umbra shadow regions. First, a novel bottom-up approach is presented based on gradient and colour models, which successfully discriminates between chromatic moving cast shadow regions and those regions detected as moving objects. In essence, those regions corresponding to potential shadows are detected based on edge partitioning and colour statistics. Subsequently (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for each potential shadow region for detecting the umbra shadow regions. Our second contribution refines even further the segmentation results: a tracking-based top-down approach increases the performance of our bottom-up chromatic shadow detection algorithm by properly correcting non-detected shadows. To do so, a combination of motion filters in a data association framework exploits the temporal consistency between objects and shadows to increase the shadow detection rate. Experimental results exceed current state-of-theart in shadow accuracy for multiple well-known surveillance image databases

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Section: Moving Foreground Segmentationmentioning

confidence: 99%

Chromatic shadow detection and tracking for moving foreground segmentation

Huerta

Holte

Moeslund

et al. 2015

Image and Vision Computing

Self Cite

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“…An example of the motion segmentation results obtained from CLEAR06 database can be seen in the Even though many of the problems of motion segmentation are solved by the approach presented in [28], the detection of moving objects in complex environments is still far from being completely solved [35] since noise and other segmentation errors occur frequently. However our system is robust to such errors thanks to the refinement of the global discriminative optimization, as described next.…”

Section: Initializationmentioning

confidence: 99%

“…In terms of computational complexity, the motion segmentation has a cost that is linear in the number of the pixels in the image. The specific implementation used in the experiments [28] runs at around 3 fps in matlab. However, a faster reimplementation or the use of other algorithm [29,30,31] can lead to more than real-time performance.…”

Section: Latent Variablesmentioning

confidence: 99%

Combining where and what in change detection for unsupervised foreground learning in surveillance

Huerta

Pedersoli

Gonzàlez³

et al. 2015

Pattern Recognition

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Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in Preprint submitted to Pattern Recognition November 3, 2014 an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent Support Vector Machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art.

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“…The Sobel [5,[7][8][9][10][11][12] and Canny [5,[13][14][15][16] operators have been employed with greater frequency for this purpose than the SUSAN edge detector [17]. While edge energy was employed in [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Patch-based local histograms and contour estimation for static foreground classification

Pereira

Saotome

Sampaio

2015

J Image Video Proc.

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This paper presents an approach to classify static foreground blobs in surveillance scenarios. Possible application is the detection of abandoned and removed objects. In order to classify the blobs, we developed two novel features based on the assumption that the neighborhood of a removed object is fairly continuous. In other words, there is a continuity, in the input frame, ranging from inside the corresponding blob contour to its surrounding region. Conversely, it is usual to find a discontinuity, i.e., edges, surrounding an abandoned object. We combined the two features to provide a reliable classification. In the first feature, we use several local histograms as a measure of similarity instead of previous attempts that used a single one. In the second, we developed an innovative method to quantify the ratio of the blob contour that corresponds to actual edges in the input image. A representative set of experiments shows that the proposed approach can outperform other equivalent techniques published recently.

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Exploiting multiple cues in motion segmentation based on background subtraction

Cited by 21 publications

References 53 publications

Chromatic shadow detection and tracking for moving foreground segmentation

Chromatic shadow detection and tracking for moving foreground segmentation

Combining where and what in change detection for unsupervised foreground learning in surveillance

Patch-based local histograms and contour estimation for static foreground classification

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