The Experts below are selected from a list of 71259 Experts worldwide ranked by ideXlab platform
Haiming Sun - One of the best experts on this subject based on the ideXlab platform.
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small scale pedestrian detection based on topological line localization and Temporal Feature aggregation
European Conference on Computer Vision, 2018Co-Authors: Tao Song, Leiyu Sun, Di Xie, Haiming SunAbstract:A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should partially resort to deep-rooted annotation bias. Motivated by this, we propose a novel method integrated with somatic topological line localization (TLL) and Temporal Feature aggregation for detecting multi-scale pedestrians, which works particularly well with small-scale pedestrians that are relatively far from the camera. Moreover, a post-processing scheme based on Markov Random Field (MRF) is introduced to eliminate ambiguities in occlusion cases. Applying with these methodologies comprehensively, we achieve best detection performance on Caltech benchmark and improve performance of small-scale objects significantly (miss rate decreases from 74.53% to 60.79%). Beyond this, we also achieve competitive performance on CityPersons dataset and show the existence of annotation bias in KITTI dataset.
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small scale pedestrian detection based on somatic topology localization and Temporal Feature aggregation
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Tao Song, Leiyu Sun, Di Xie, Haiming SunAbstract:A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should partially resort to deep-rooted annotation bias. Motivated by this, we propose a novel method integrated with somatic topological line localization (TLL) and Temporal Feature aggregation for detecting multi-scale pedestrians, which works particularly well with small-scale pedestrians that are relatively far from the camera. Moreover, a post-processing scheme based on Markov Random Field (MRF) is introduced to eliminate ambiguities in occlusion cases. Applying with these methodologies comprehensively, we achieve best detection performance on Caltech benchmark and improve performance of small-scale objects significantly (miss rate decreases from 74.53% to 60.79%). Beyond this, we also achieve competitive performance on CityPersons dataset and show the existence of annotation bias in KITTI dataset.
Tao Song - One of the best experts on this subject based on the ideXlab platform.
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small scale pedestrian detection based on topological line localization and Temporal Feature aggregation
European Conference on Computer Vision, 2018Co-Authors: Tao Song, Leiyu Sun, Di Xie, Haiming SunAbstract:A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should partially resort to deep-rooted annotation bias. Motivated by this, we propose a novel method integrated with somatic topological line localization (TLL) and Temporal Feature aggregation for detecting multi-scale pedestrians, which works particularly well with small-scale pedestrians that are relatively far from the camera. Moreover, a post-processing scheme based on Markov Random Field (MRF) is introduced to eliminate ambiguities in occlusion cases. Applying with these methodologies comprehensively, we achieve best detection performance on Caltech benchmark and improve performance of small-scale objects significantly (miss rate decreases from 74.53% to 60.79%). Beyond this, we also achieve competitive performance on CityPersons dataset and show the existence of annotation bias in KITTI dataset.
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small scale pedestrian detection based on somatic topology localization and Temporal Feature aggregation
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Tao Song, Leiyu Sun, Di Xie, Haiming SunAbstract:A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should partially resort to deep-rooted annotation bias. Motivated by this, we propose a novel method integrated with somatic topological line localization (TLL) and Temporal Feature aggregation for detecting multi-scale pedestrians, which works particularly well with small-scale pedestrians that are relatively far from the camera. Moreover, a post-processing scheme based on Markov Random Field (MRF) is introduced to eliminate ambiguities in occlusion cases. Applying with these methodologies comprehensively, we achieve best detection performance on Caltech benchmark and improve performance of small-scale objects significantly (miss rate decreases from 74.53% to 60.79%). Beyond this, we also achieve competitive performance on CityPersons dataset and show the existence of annotation bias in KITTI dataset.
Wissam J Baddar - One of the best experts on this subject based on the ideXlab platform.
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multi objective based spatio Temporal Feature representation learning robust to expression intensity variations for facial expression recognition
IEEE Transactions on Affective Computing, 2019Co-Authors: Dae Hoe Kim, Wissam J Baddar, Jinhyeok JangAbstract:Facial expression recognition (FER) is increasingly gaining importance in various emerging affective computing applications. In practice, achieving accurate FER is challenging due to the large amount of inter-personal variations such as expression intensity variations. In this paper, we propose a new spatio-Temporal Feature representation learning for FER that is robust to expression intensity variations. The proposed method utilizes representative expression-states (e.g., onset, apex and offset of expressions) which can be specified in facial sequences regardless of the expression intensity. The characteristics of facial expressions are encoded in two parts in this paper. As the first part, spatial image characteristics of the representative expression-state frames are learned via a convolutional neural network. Five objective terms are proposed to improve the expression class separability of the spatial Feature representation. In the second part, Temporal characteristics of the spatial Feature representation in the first part are learned with a long short-term memory of the facial expression. Comprehensive experiments have been conducted on a deliberate expression dataset (MMI) and a spontaneous micro-expression dataset (CASME II). Experimental results showed that the proposed method achieved higher recognition rates in both datasets compared to the state-of-the-art methods.
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micro expression recognition with expression state constrained spatio Temporal Feature representations
ACM Multimedia, 2016Co-Authors: Dae Hoe Kim, Wissam J BaddarAbstract:Recognizing spontaneous micro-expression in video sequences is a challenging problem. In this paper, we propose a new method of small scale spatio-Temporal Feature learning. The proposed learning method consists of two parts. First, the spatial Features of micro-expressions at different expression-states (i.e., onset, onset to apex transition, apex, apex to offset transition and offset) are encoded using convolutional neural networks (CNN). The expression-states are taken into account in the objective functions, to improve the expression class separability of the learned Feature representation. Next, the learned spatial Features with expression-state constraints are transferred to learn Temporal Features of micro-expression. The Temporal Feature learning encodes the Temporal characteristics of the different states of the micro-expression using long short-term memory (LSTM) recurrent neural networks. Extensive and comprehensive experiments have been conducted on the publically available CASME II micro-expression dataset. The experimental results showed that the proposed method outperformed state-of-the-art micro-expression recognition methods in terms of recognition accuracy.
Guang Han - One of the best experts on this subject based on the ideXlab platform.
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deep spatial Temporal Feature fusion for facial expression recognition in static images
Pattern Recognition Letters, 2017Co-Authors: Ning Sun, Ruizhi Huan, Jixin Liu, Guang HanAbstract:ABSTRACT Traditional methods of performing facial expression recognition commonly use hand-crafted spatial Features. This paper proposes a multi-channel deep neural network that learns and fuses the spatial-Temporal Features for recognizing facial expressions in static images. The essential idea of this method is to extract optical flow from the changes between the peak expression face image (emotional-face) and the neutral face image (neutral-face) as the Temporal information of a certain facial expression, and use the gray-level image of emotional-face as the spatial information. A Multi-channel Deep Spatial-Temporal Feature Fusion neural Network (MDSTFN) is presented to perform the deep spatial-Temporal Feature extraction and fusion from static images. Each channel of the proposed method is fine-tuned from a pre-trained deep convolutional neural networks (CNN) instead of training a new CNN from scratch. In addition, average-face is used as a substitute for neutral-face in real-world applications. Extensive experiments are conducted to evaluate the proposed method on benchmarks databases including CK+, MMI, and RaFD. The results show that the optical flow information from emotional-face and neutral-face is a useful complement to spatial Feature and can effectively improve the performance of facial expression recognition from static images. Compared with state-of-the-art methods, the proposed method can achieve better recognition accuracy, with rates of 98.38% on the CK+ database, 99.17% on the RaFD database, and 99.59% on the MMI database, respectively.
Dae Hoe Kim - One of the best experts on this subject based on the ideXlab platform.
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multi objective based spatio Temporal Feature representation learning robust to expression intensity variations for facial expression recognition
IEEE Transactions on Affective Computing, 2019Co-Authors: Dae Hoe Kim, Wissam J Baddar, Jinhyeok JangAbstract:Facial expression recognition (FER) is increasingly gaining importance in various emerging affective computing applications. In practice, achieving accurate FER is challenging due to the large amount of inter-personal variations such as expression intensity variations. In this paper, we propose a new spatio-Temporal Feature representation learning for FER that is robust to expression intensity variations. The proposed method utilizes representative expression-states (e.g., onset, apex and offset of expressions) which can be specified in facial sequences regardless of the expression intensity. The characteristics of facial expressions are encoded in two parts in this paper. As the first part, spatial image characteristics of the representative expression-state frames are learned via a convolutional neural network. Five objective terms are proposed to improve the expression class separability of the spatial Feature representation. In the second part, Temporal characteristics of the spatial Feature representation in the first part are learned with a long short-term memory of the facial expression. Comprehensive experiments have been conducted on a deliberate expression dataset (MMI) and a spontaneous micro-expression dataset (CASME II). Experimental results showed that the proposed method achieved higher recognition rates in both datasets compared to the state-of-the-art methods.
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micro expression recognition with expression state constrained spatio Temporal Feature representations
ACM Multimedia, 2016Co-Authors: Dae Hoe Kim, Wissam J BaddarAbstract:Recognizing spontaneous micro-expression in video sequences is a challenging problem. In this paper, we propose a new method of small scale spatio-Temporal Feature learning. The proposed learning method consists of two parts. First, the spatial Features of micro-expressions at different expression-states (i.e., onset, onset to apex transition, apex, apex to offset transition and offset) are encoded using convolutional neural networks (CNN). The expression-states are taken into account in the objective functions, to improve the expression class separability of the learned Feature representation. Next, the learned spatial Features with expression-state constraints are transferred to learn Temporal Features of micro-expression. The Temporal Feature learning encodes the Temporal characteristics of the different states of the micro-expression using long short-term memory (LSTM) recurrent neural networks. Extensive and comprehensive experiments have been conducted on the publically available CASME II micro-expression dataset. The experimental results showed that the proposed method outperformed state-of-the-art micro-expression recognition methods in terms of recognition accuracy.
