Kernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background

Zhang, Baochang; Gao, Yongsheng; Zhao, Sanqiang; Zhong, Bineng

doi:10.1109/tcsvt.2011.2105591

Cited by 27 publications

(27 citation statements)

References 19 publications

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“…Scene modeling is an important subject in many different applications of outdoor video analysis and computer vision, such as motion detection [1], object tracking [2], video surveillance [3], robot navigation [4], and video retrieval [5]. Previous research in this field has underestimated the importance of weather conditions for modeling, revealing that the applications mentioned above appear poor results when weather condition becomes to change.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction for classification of different weather conditions

Zhao

Liu

et al. 2011

Front. Electr. Electron. Eng. China

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Classification of different weather conditions provides a first step support for outdoor scene modeling, which is a core component in many different applications of outdoor video analysis and computer vision. Features derived from intrinsic properties of the visual effects of different weather conditions contribute to successful classification. In this paper, features representing both the autocorrelation of pixel-wise intensities over time and the max directional length of rain streaks or snowflakes are proposed. Based on the autocorrelation of each pixel's intensities over time, two temporal features are used for coarse classification of weather conditions according to their visual effects. On the other hand, features are extracted for fine classification of video clips with rain and snow. The classification results on 249 video clips associated with different weather conditions indicate the effectiveness of the extracted features, by using C-SVM as the classifier.

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Section: Introductionmentioning

confidence: 99%

Feature extraction for classification of different weather conditions

Zhao

Liu

et al. 2011

Front. Electr. Electron. Eng. China

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“…Our work shares the same end goal of automated video object segmentation [1], [2], [19], [20], [21], [22], [23], [24], but our approach is more inline with research that places humans in the loop (including games with purpose) [11], [12], [13], [14], [15], [16], [17], [25], [26], [27], [28], [29], [30], [31], [32] and interactive video segmentation [7], [33], [34], [35], [36], [37], [38], [39].…”

Section: Related Workmentioning

confidence: 99%

“…Unsupervised video segmentation has gained a lot of attention in the last decades [2], [19], [20], [21], [22] and recently it has been thought mainly in terms of spatiotemporal superpixel modeling [1], [23], [24]. The key idea behind these methods is the one of grouping pixels which are appearance-and motion-wise-consistent.…”

Section: Related Workmentioning

confidence: 99%

Gamifying Video Object Segmentation

Spampinato

Palazzo

Giordano

2017

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Video object segmentation can be considered as one of the most challenging computer vision problems. Indeed, so far, no existing solution is able to effectively deal with the peculiarities of real-world videos, especially in cases of articulated motion and object occlusions; limitations that appear more evident when we compare their performance with the human one. However, manually segmenting objects in videos is largely impractical as it requires a lot of human time and concentration. To address this problem, in this paper we propose an interactive video object segmentation method, which exploits, on one hand, the capability of humans to identify correctly objects in visual scenes, and on the other hand, the collective human brainpower to solve challenging tasks. In particular, our method relies on a web game to collect human inputs on object locations, followed by an accurate segmentation phase achieved by optimizing an energy function encoding spatial and temporal constraints between object regions as well as human-provided input. Performance analysis carried out on challenging video datasets with some users playing the game demonstrated that our method shows a better trade-off between annotation times and segmentation accuracy than interactive video annotation and automated video object segmentation approaches.Index Terms-Interactive video annotation, Games with a purpose, Human in the Loop, Spatio-temporal superpixel segmentation

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“…To improve the MoG model some methods have used color model space [8], Markov Random Fields [9], or non parametric Kernel distributions [10], [11]. Other methods encode pixel changes in the spatiotemporal space [12], [13], [14], [15], [16], [17], [18], [19], while other schemes account explicitly for conditions such as illumination changes [20], non-stationary camera [21], or non-stationary background textures [22]. All of these methods require sensing for a period of time to estimate the model parameters, which must then be continually updated.…”

Section: Introductionmentioning

confidence: 99%

Robust Background Subtraction Using Geodesic Active Contours in ICA Subspace for Video Surveillance Applications

Sekkati

Laganière

Mitiche³

et al. 2012

2012 Ninth Conference on Computer and Robot Vision

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Abstract-Current background subtraction methods require background modeling to handle dynamic backgrounds. The purpose of our study is to investigate a background template substraction method to detect foreground objects in the presence of background variations. The method uses a single reference image but the change detection process allows change in the background including illumination changes and dynamic scenes. Using indoor and outdoor scenes, we compare our method to the best state-of-the art algorithms using both quantitative and qualitative evaluation. The results show that our method is in general more accurate and more effective.

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Kernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background

Cited by 27 publications

References 19 publications

Feature extraction for classification of different weather conditions

Feature extraction for classification of different weather conditions

Gamifying Video Object Segmentation

Robust Background Subtraction Using Geodesic Active Contours in ICA Subspace for Video Surveillance Applications

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