Deep learning to frame objects for visual target tracking

Pang, Shuchao; Coz, Juan José del; Yu, Zhezhou; Luaces, Oscar; Díez, Jorge

doi:10.1016/j.engappai.2017.08.010

Cited by 45 publications

(30 citation statements)

References 31 publications

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“…In the past few years, deep learning has already swept through most research fields of computer vision and has achieved better results than traditional methods, and biomedical/medical image segmentation is also no exception [11,12]. The existing methods [13][14][15][16] strive to obtain more precise and comprehensive tumor features by taking the advantage of deep learning in its superior ability of hierarchical feature representation.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Fast and Accurate Lung Tumor Spotting and Segmentation for Boundary Delineation on CT Slices in a Coarse-to-Fine Framework

Pang

et al. 2019

Communications in Computer and Information Science

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Label noise and class imbalance are two of the critical challenges when training image-based deep neural networks, especially in the biomedical image processing domain. Our work focuses on how to effectively and accurately address the two challenges in the task of lesion segmentation from biomedical/medical images. To address the pixel-level label noise problem, an advanced transfer training and learning approach with a detailed DICOM pre-processing work is proposed in this paper. To address the tumor/non-tumor class imbalance problem, we exploit a self-adaptive fully convolutional neural network with an automated weight distribution mechanism to accurately spot the Radiomics lung tumor regions. Furthermore, an improved conditional random field method is utilized to obtain a sophisticated lung tumor contour delineation and segmentation. Finally, our approach has been evaluated using several well-known evaluation metrics on the Radiomics Lung Tumor segmentation dataset used in the 2018 IEEE VIP-CUP Challenge, which totally contains 422 patient records with 51513 Radiomics Lung slices. Experimental results show that our weakly supervised learning algorithm outperforms other deep models and state-of-the-art approaches.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Fast and Accurate Lung Tumor Spotting and Segmentation for Boundary Delineation on CT Slices in a Coarse-to-Fine Framework

Pang

et al. 2019

Communications in Computer and Information Science

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“…CNN is defined as a multi-layer neural network with a unique architecture used for deep learning [12]. A CNN architecture is comprised of three essential layers: Convolutional Layer, Pooling Layer, and Fully-Connected Layer.…”

Section: Methodsmentioning

confidence: 99%

“…CNN is generally used in recognizing objects, scenes, and carrying out image detection, extraction and segmentation. CNN has been significantly used in the last few years ago due to the following three aspects: (1) the necessity for feature extraction by using image processing tools is removed since CNN can directly learn the image data, (2) Exceptionally good for recognition of results and can be easily re-trained for new recognition purposes, and (3) CNN can be built on the preexisting network [12].…”

Section: Methodsmentioning

confidence: 99%

Hand Gesture Recognition Using Convolutional Neural Network for People Who Have Experienced A Stroke

Alnaim

Abbod

Albar

2019

2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

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A human gesture is a non-verbal form of communication and is critical in human-robot interactions. Vision-based gesture recognition methods play a key role to detect hand motion and support such interactions. Hand gesture recognition allows a convenient and usable interface between devices and users. Hand gestures can be used for various fields which makes it be able to be implemented for communication and further. Hand gesture recognition is not only useful for people who are hearingimpaired or disabled but also for the people who experienced a stroke, as they need to communicate with other people using different common essential gestures such as the sign of eating, drink, family and, more. In this paper, an approach for recognizing hand gesture based on Convolutional Neural Network (CNN) is proposed. The developed method is evaluated and compared between training and testing modes based on several metrics such as execution time, accuracy, sensitivity, specificity, positive and negative predictive value, likelihood and root mean square. Results show that testing accuracy is 99% using CNN and is an effective technique in extracting distinct features and classifying data.

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“…The proposed system outperforms the state-of-the-art systems. Schuchao et al [21] proposed a deep learning-based technique for tracking visual objects. They used CNN to rank the patches of the target objects based on how well it is centred.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Video Surveillance Using VGG19 Convolutional Neural Networks

Butt¹,

Letchmunan²,

Hafinaz³

et al. 2020

IJACSA

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The meteoric growth of data over the internet from the last few years has created a challenge of mining and extracting useful patterns from a large dataset. In recent years, the growth of digital libraries and video databases makes it more challenging and important to extract useful information from raw data to prevent and detect the crimes from the database automatically. Street crime snatching and theft detection is the major challenge in video mining. The main target is to select features/objects which usually occurs at the time of snatching. The number of moving targets imitates the performance, speed and amount of motion in the anomalous video. The dataset used in this paper is Snatch 101; the videos in the dataset are further divided into frames. The frames are labelled and segmented for training. We applied the VGG19 Convolutional Neural Network architecture algorithm and extracted the features of objects and compared them with original video features and objects. The main contribution of our research is to create frames from the videos and then label the objects. The objects are selected from frames where we can detect anomalous activities. The proposed system is never used before for crime prediction, and it is computationally efficient and effective as compared to state-of-the-art systems. The proposed system outperformed with 81 % accuracy as compared to stateof-the-art systems.

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Deep learning to frame objects for visual target tracking

Cited by 45 publications

References 31 publications

Fast and Accurate Lung Tumor Spotting and Segmentation for Boundary Delineation on CT Slices in a Coarse-to-Fine Framework

Fast and Accurate Lung Tumor Spotting and Segmentation for Boundary Delineation on CT Slices in a Coarse-to-Fine Framework

Hand Gesture Recognition Using Convolutional Neural Network for People Who Have Experienced A Stroke

Detecting Video Surveillance Using VGG19 Convolutional Neural Networks

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