Foreground Detection with Deeply Learned Multi-Scale Spatial-Temporal Features

Wang, Yao; Yu, Zhiwu; Zhu, Liqiang

doi:10.3390/s18124269

Cited by 19 publications

(14 citation statements)

References 46 publications

(76 reference statements)

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“…Recently [3,4,16,17], convolutional neural networks (ConvNets) have presented excellent results in different vision challenges where it has shown an attractive characteristic to learn deep and hierarchical features, which make it more powerful than classical methods. In this work, two convolution layers, two max-pooling layers, and two fully connected feed-forward layers are adopted with the same network architecture in [16], which obtained better detection results by discriminating the foreground and background regions.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Robust Vehicle Detection and Counting Algorithm Employing a Convolution Neural Network and Optical Flow

Gomaa

Abdelwahab

Abo‐Zahhad

et al. 2019

Sensors

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Automatic vehicle detection and counting are considered vital in improving traffic control and management. This work presents an effective algorithm for vehicle detection and counting in complex traffic scenes by combining both convolution neural network (CNN) and the optical flow feature tracking-based methods. In this algorithm, both the detection and tracking procedures have been linked together to get robust feature points that are updated regularly every fixed number of frames. The proposed algorithm detects moving vehicles based on a background subtraction method using CNN. Then, the vehicle’s robust features are refined and clustered by motion feature points analysis using a combined technique between KLT tracker and K-means clustering. Finally, an efficient strategy is presented using the detected and tracked points information to assign each vehicle label with its corresponding one in the vehicle’s trajectories and truly counted it. The proposed method is evaluated on videos representing challenging environments, and the experimental results showed an average detection and counting precision of 96.3% and 96.8%, respectively, which outperforms other existing approaches.

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Section: Methodsmentioning

confidence: 99%

“…The most recent studies in intelligent transportation systems focus on vehicle detection [3,4,5,6,7,8,9,10,11,12]. Vehicle detection can be categorized into two groups [1]: detection methods based on vehicle appearance, and detection methods based on vehicle motion.…”

Section: Introductionmentioning

confidence: 99%

Robust Vehicle Detection and Counting Algorithm Employing a Convolution Neural Network and Optical Flow

Gomaa

Abdelwahab

Abo‐Zahhad

et al. 2019

Sensors

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“…The dilated convolution is widely used in image feature extraction due to its good performance in extracting powerful features with various receptive fields from image [33]. The dilated convolution applies a filter with different convolutional regions by employing different dilation factors, which can expand the receptive fields without losing the resolution or coverage.…”

Section: B Dilated Convolutionmentioning

confidence: 99%

Multi-View Convolutional Neural Networks for Mammographic Image Classification

Sun

Wang

et al. 2019

IEEE Access

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In recent years, deep learning has been widely applied for mammographic image classification. However, most of the existing methods are based on a single mammography view and cannot sufficiently extract discriminative features, thereby resulting in an unsatisfactory classification accuracy. To solve this problem and improve the mammographic image classification performance, we propose a novel multi-view convolutional neural network (CNN) based on multiple mammography views in this paper. Considering that the images acquired from different perspectives contain different and complementary breast mass information, we modify the CNN architecture to exploit the complementary information from the various views of mammography. The new architecture can extract discriminative features from the mediolateral oblique (MLO) and craniocaudal (CC) views of the mammographic images and can effectively incorporate these features for mammographic images. The dilated convolutional layers enable the feature maps extracted from the multiple breast mass views to capture information from a large ''field of vision''. Moreover, multiscale features are obtained by using the convolutional and dilated convolutions. In addition, we incorporate a penalty term into the cross entropy loss function, which enables the model evolution to reduce the misclassification rate by enhancing the contributions of the samples misclassified in the training process. The proposed method was evaluated and compared with several state-of-the-art methods on the open Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) datasets. The experimental results show that the proposed method exhibits a better performance than those of the state-of-the-art methods.INDEX TERMS Medical image processing, mammographic image, deep learning, convolutional neural network.

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“…These descriptors [5]- [9] have been proven to be able to represent video information effectively. Recently, the Convolutional Neural Networks (CNN) [10] based approaches have been widely used for computer vision, such as image classification [11], [12], image segmentation [13], face recognition [14], [15], foreground detection [16], target tracking [17], [18], etc. CNNs have been proven that they can efficiently extract spatial features from static image.…”

Section: Introductionmentioning

confidence: 99%

Action Recognition Based on Two-Stream Convolutional Networks With Long-Short-Term Spatiotemporal Features

Wan

Wang

et al. 2020

IEEE Access

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Human action recognition is an important research topic in the field of computer vision due to its application values. Recently, a variety of approaches based on deep learning features have been proposed due to the effectiveness of deep neural networks. But most of these approaches are not able to fully extract spatiotemporal features from videos, because of the lack of consideration of the diversity of scales in temporal domain. In this paper, we propose a two-stream convolutional network with long-short-term spatiotemporal features (LSF CNN) for human action recognition task. The network is mainly composed of two subnetworks. One is long-term spatiotemporal features extraction network (LT-Net) that takes the stacked RGB images as inputs. Another one is short-term spatiotemporal features extraction network (ST-Net) that takes the optical flow as input, which is estimated from two adjacent frames. The two-scale spatiotemporal features are fused in the fully-connected layer and fed into the linear support vector machine (SVM). We also propose a new expression for optical flow field, which is proved to have better performance than traditional expression in action recognition problem. With two-stream architecture, the network can fully learn deep features in both spatial and temporal domains. The experimental results on HMDB51 and UCF101 datasets indicated that the proposed approach improves the action recognition accuracy by using the long-short-term spatiotemporal information.

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Foreground Detection with Deeply Learned Multi-Scale Spatial-Temporal Features

Cited by 19 publications

References 46 publications

Robust Vehicle Detection and Counting Algorithm Employing a Convolution Neural Network and Optical Flow

Robust Vehicle Detection and Counting Algorithm Employing a Convolution Neural Network and Optical Flow

Multi-View Convolutional Neural Networks for Mammographic Image Classification

Action Recognition Based on Two-Stream Convolutional Networks With Long-Short-Term Spatiotemporal Features

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