Spatiotemporal Saliency Detection and Its Applications in Static and Dynamic Scenes

Kim, Wonjun; Jung, Chanho; Kim, Changick

doi:10.1109/tcsvt.2011.2125450

Cited by 99 publications

(74 citation statements)

References 33 publications

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“…Table 1 summarizes the results obtained with all sequences by the different fusion techniques. We observe that the best performances are obtained by the Mean [12], Scale Invariant [22], Max [2] and Dynamic Weight [20] fusion methods respectively. In particular, the Mean fusion technique achieves an average AUC value of 0.9325 for all twelve sequences.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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A performance evaluation of fusion techniques for spatio-temporal saliency detection in dynamic scenes

Muddamsetty

Sidibé

Trémeau

et al. 2013

2013 IEEE International Conference on Image Processing

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Visual saliency is an important research topic in computer vision applications, which helps to focus on regions of interest instead of processing the whole image. Detecting visual saliency in still images has been widely addressed in literature. However, visual saliency detection in videos is more complicated due to additional temporal information. A spatio-temporal saliency map is usually obtained by the fusion of a static saliency map and a dynamic saliency map. The way both maps are fused plays a critical role in the accuracy of the spatio-temporal saliency map. In this paper, we evaluate the performances of different fusion techniques on a large and diverse dataset and the results show that a fusion method must be selected depending on the characteristics, in terms of color and motion contrasts, of a sequence. Overall, fusion techniques which take the best of each saliency map (static and dynamic) in the final spatio-temporal map achieve best results.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…Scale invariant fusion [22]: in this fusion technique, the input images are analyzed at three different scales from 32 × 32 to 128 × 128 to original image size. Three fused maps are obtained which are finally combined linearly into the final spatio-temporal saliency map.…”

Section: Fusion Techniquesmentioning

confidence: 99%

A performance evaluation of fusion techniques for spatio-temporal saliency detection in dynamic scenes

Muddamsetty

Sidibé

Trémeau

et al. 2013

2013 IEEE International Conference on Image Processing

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“…In [6] Wonjun Kim, Chanho Jung, and Changick Kim suggested a novel unified method for detecting salient regions in both images and videos based on a discriminant centersurround hypothesis that the salient region stands out from its surroundings. First of all, a set of visual features composed of edge and color orientations and temporal gradients are computed.…”

Section: Figure 2: Framework Of Saliency Detection Model Based On Wavmentioning

confidence: 99%

A Survey on Various Saliency Detection Methods

Sebastian¹,

Daniel²

2017

IJCA

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Visual saliency is an important characteristic of Human Visual System (HVS) that select the visually significant information from scenes. The salient objects stand out relative to their neighbourhood regions. Detecting and segmenting salient objects, also known as salient object detection is used to extract the most interesting object/objects in a scene and has resulted in many applications. There are many different methods to detect saliency known as visual attention models or saliency detection methods. In past few years many saliency detection methods have been proposed. One of the main objectives of the work is to perform a detail study in the field of Saliency detection by keeping focus on the different bottom-up computational models and the methods used to predict saliency. The work aims to analyze various solutions that aid the task of HVSs properties. This paper presents various saliency detection methods.

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“…F 3,4 Signed distance between R/L-foot and the plane defined by L-shoulder, R-shoulder, and R/L-pelvis.…”

Section: Grf Conversionmentioning

confidence: 99%

Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs

Thuc

Hwang

et al. 2014

ETRI J

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Human action recognition is used in areas such as surveillance, entertainment, and healthcare. This paper proposes a system to recognize both single and continuous human actions from monocular video sequences, based on 3D human modeling and cyclic hidden Markov models (CHMMs). First, for each frame in a monocular video sequence, the 3D coordinates of joints belonging to a human object, through actions of multiple cycles, are extracted using 3D human modeling techniques. The 3D coordinates are then converted into a set of geometrical relational features (GRFs) for dimensionality reduction and discrimination increase. For further dimensionality reduction, k-means clustering is applied to the GRFs to generate clustered feature vectors. These vectors are used to train CHMMs separately for different types of actions, based on the Baum-Welch re-estimation algorithm. For recognition of continuous actions that are concatenated from several distinct types of actions, a designed graphical model is used to systematically concatenate different separately trained CHMMs. The experimental results show the effective performance of our proposed system in both single and continuous action recognition problems. Keywords I. IntroductionHuman action recognition is a growing topic in video analysis and understanding -one of the most popular areas in the community of computer vision -thanks to its applications in surveillance, entertainment, and healthcare. In surveillance, human action recognition can be used in conjunction with video camera footage to help with the recognition and analysis of human actions. In entertainment, human-computer interaction can be helped to appear more natural via human action recognition, which in turn can help increase the entertainment experience. In healthcare, human action recognition can help detect abnormal gaits or assist in a patient's rehabilitation through an analysis of their actions.However, it is challenging to recognize various human actions due to the high number of degrees of freedom associated with the average human body -namely, variations in human poses; variations in the colors of a person's clothing; changes in lighting and illumination; variations in viewpoints; and frequent self-occlusion. Moreover, the use of monocular video sequences further increases the difficulty for human action recognition.Generally, the two main stages in human action recognition are: the feature extraction and representation stage and the classification stage.In the feature extraction and representation stage, the features or characteristics of video frames, such as silhouette, shape, color, and motion, are extracted and represented in a systematic and efficient way. consists of stacking segmented silhouettes (frame by frame) to form a 3D spatial-temporal shape. In a similar way, Ke and others [2] build STVs, for shape-based matching, from image features that are based on the consecutive silhouettes of objects along a time axis, including spatial-temporal region extraction and region matching. Kim and oth...

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Spatiotemporal Saliency Detection and Its Applications in Static and Dynamic Scenes

Cited by 99 publications

References 33 publications

A performance evaluation of fusion techniques for spatio-temporal saliency detection in dynamic scenes

A performance evaluation of fusion techniques for spatio-temporal saliency detection in dynamic scenes

A Survey on Various Saliency Detection Methods

Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs

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