A Pose-Invariant Descriptor for Human Detection and Segmentation

Lin, Zhe; Davis, Larry S.

doi:10.1007/978-3-540-88693-8_31

Cited by 109 publications

(65 citation statements)

References 15 publications

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“…In this part, we compare the proposed method with other state-of-the-art methods evaluated in [14], including: Viola-Jones [40], Shapelet [44], LatSVM-V1, LatSVM-V2 [19], PoseInv [30], HOGLbp [43], HikSVM [31], HOG [6], FtrMine [13], MultFtr [44], MultiFtr+CSS [44], Pls [37], MultiFtr+Motion [44], FPDW [11], FeatSynth [1], ChnFtrs [12], MultiResC [33]. The results of the proposed methods are denoted as MT-DPM, and MT-DPM+ Context.…”

Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

Robust Multi-resolution Pedestrian Detection in Traffic Scenes

Yan

Zhang

Lei

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

133

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The serious performance decline with decreasing resolution is the major bottleneck for current pedestrian detection techniques [14,23]. In this paper, we take pedestrian detection in different resolutions as different but related problems, and propose a Multi-Task model to jointly consider their commonness and differences. The model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detector is constructed to distinguish pedestrians from background. For model learning, we present a coordinate descent procedure to learn the resolution aware transformations and deformable part model (DPM) based detector iteratively. In traffic scenes, there are many false positives located around vehicles, therefore, we further build a context model to suppress them according to the pedestrian-vehicle relationship. The context model can be learned automatically even when the vehicle annotations are not available. Our method reduces the mean miss rate to 60% for pedestrians taller than 30 pixels on the Caltech Pedestrian Benchmark, which noticeably outperforms previous state-of-the-art (71%).

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Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

Robust Multi-resolution Pedestrian Detection in Traffic Scenes

Yan

Zhang

Lei

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

133

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“…Another popular approach is the combination of local and global cues via a probabilistic top-down segmentation [28]. A combination of multiple features such as silhouette, appearance, holistic, and part-based can be used as input to a SVM classifier [27] [47]. The Viola and Jones algorithm, used initially in face detection, can be trained for pedestrian detection [48].…”

Section: A Appearance-based Methodsmentioning

confidence: 99%

Video-Based Human Behavior Understanding: A Survey

Borges

Conci

Cavallaro

2013

IEEE Trans. Circuits Syst. Video Technol.

188

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Abstract-Understanding human behaviors is a challenging problem in computer vision that has recently seen important advances. Human behavior understanding combines image and signal processing, feature extraction, machine learning and 3D geometry. Application scenarios range from surveillance to indexing and retrieval, from patient care to industrial safety and sports analysis. Given the broad set of techniques used in videobased behavior understanding and the fast progress in this area, in this paper we organize and survey the corresponding literature, define unambiguously key terms and discuss links among fundamental building blocks ranging from human detection to action and interaction recognition. The advantages and the drawbacks of the methods are critically discussed, providing a comprehensive coverage of key aspects of video-based human behavior understanding, available datasets for experimentation and comparisons, and important open research issues.

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“…The moving areas through the video frames can be used to detect the area of interest (motion area) [A07,LD08].…”

Section: Literature Reviewmentioning

confidence: 99%

Human Action Recognition from RGBD Videos based on Retina Model and Local Binary Pattern Features

Al-Akam¹,

Al-Darraji²,

Paulus³

2018

CSRN

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Human action recognition from the videos is one of the most attractive topics in computer vision during the last decades due to wide applications development. This research has mainly focused on learning and recognizing actions from RGB and Depth videos (RGBD). RGBD is a powerful source of data providing the aligned depth information which has great ability to improve the performance of different problems in image understanding and video processing. In this work, a novel system for human action recognition is proposed to extract distinctive spatio and temporal feature vectors for presenting the spatio-temporal evolutions from a set of training and testing video sequences of different actions. The feature vectors are computed in two steps: The First step is the motion detection from all video frames by using spatio-temporal retina model. This model gives a good structuring of video data by removing the noise and illumination variation and is used to detect potentially salient areas, these areas represent the motion information of the moving object in each frame of video sequences. In the Second step, because of human motion can be seen as a type of texture pattern, the local binary pattern descriptor (LBP) is used to extract features from the spatio-temporal salient areas and formulated them as a histogram to make the bag of feature vectors. To evaluate the performance of the proposed method, the k-means clustering, and Random Forest classification is applied on the bag of feature vectors. This approach is demonstrated that our system achieves superior performance in comparison with the state-of-the-art and all experimental results are depending on two public RGBD datasets.

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A Pose-Invariant Descriptor for Human Detection and Segmentation

Cited by 109 publications

References 15 publications

Robust Multi-resolution Pedestrian Detection in Traffic Scenes

Robust Multi-resolution Pedestrian Detection in Traffic Scenes

Video-Based Human Behavior Understanding: A Survey

Human Action Recognition from RGBD Videos based on Retina Model and Local Binary Pattern Features

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