A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification

Due to their high distinctiveness, robustness to illumination and simple computation, Histogram of Oriented Gradient (HOG) features have attracted much attention and achieved remarkable success in many computer vision tasks. In this paper, an innovative framework for driver drowsiness detection is proposed, where an adaptive descriptor that possesses the virtue of distinctiveness, robustness and compactness is formed from an improved version of HOG features based on binarized histograms of shifted orientations. The final HOG descriptor generated from binarized HOG features is fed to the trained Naïve Bayes (NB) classifier to make the final driver drowsiness determination. Experimental results on the publicly available NTHU-DDD dataset verify that the proposed framework has the potential to be a strong contender for several state-of-the-art baselines, by achieving a competitive detection accuracy of 85.62%, without loss of efficiency or stability.

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Random Forest with Adaptive Local Template for Pedestrian Detection

Mathematical Problems in Engineering ◽

10.1155/2015/767423 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Tao Xiang ◽

Tao Li ◽

Mao Ye ◽

Zijian Liu

Keyword(s):

Computer Vision ◽

Random Forest ◽

Classification Accuracy ◽

Template Matching ◽

Detection Method ◽

State Of The Art ◽

Pedestrian Detection ◽

Sliding Window ◽

Experimental Results ◽

Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

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Document-level Relation Extraction as Semantic Segmentation

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/551 ◽

2021 ◽

Author(s):

Ningyu Zhang ◽

Xiang Chen ◽

Xin Xie ◽

Shumin Deng ◽

Chuanqi Tan ◽

...

Keyword(s):

Computer Vision ◽

State Of The Art ◽

Relation Extraction ◽

Semantic Segmentation ◽

Experimental Results ◽

Context Information ◽

Global Information ◽

Benchmark Datasets ◽

Segmentation Task ◽

Document Level

Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the entities independently, regardless of global information among relational triples. This paper approaches the problem by predicting an entity-level relation matrix to capture local and global information, parallel to the semantic segmentation task in computer vision. Herein, we propose a Document U-shaped Network for document-level relation extraction. Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples. Experimental results show that our approach can obtain state-of-the-art performance on three benchmark datasets DocRED, CDR, and GDA.

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An Improved Fingerprinting Algorithm for Detection of Video Frame Duplication Forgery

International Journal of Digital Crime and Forensics ◽

10.4018/jdcf.2012070102 ◽

2012 ◽

Vol 4 (3) ◽

pp. 20-32 ◽

Cited By ~ 3

Author(s):

Yongjian Hu ◽

Chang-Tsun Li ◽

Yufei Wang ◽

Bei-bei Liu

Keyword(s):

Computational Efficiency ◽

State Of The Art ◽

Experimental Results ◽

Video Frame ◽

Detection Accuracy ◽

Forgery Detection ◽

Detection Algorithms ◽

Dct Coefficients ◽

Duplication Detection ◽

Frame Duplication

Frame duplication is a common way of digital video forgeries. State-of-the-art approaches of duplication detection usually suffer from heavy computational load. In this paper, the authors propose a new algorithm to detect duplicated frames based on video sub-sequence fingerprints. The fingerprints employed are extracted from the DCT coefficients of the temporally informative representative images (TIRIs) of the sub-sequences. Compared with other similar algorithms, this study focuses on improving fingerprints representing video sub-sequences and introducing a simple metric for the matching of video sub-sequences. Experimental results show that the proposed algorithm overall outperforms three related duplication forgery detection algorithms in terms of computational efficiency, detection accuracy and robustness against common video operations like compression and brightness change.

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Application of deep learning and computer vision frameworks for solving video context prediction problem

PROBLEMS IN PROGRAMMING ◽

10.15407/pp2016.02-03.164 ◽

2016 ◽

pp. 164-169

Author(s):

D. Voloshyn ◽

Keyword(s):

Computer Vision ◽

Deep Learning ◽

State Of The Art ◽

Experimental Results ◽

Prediction Problem ◽

Detection Problem ◽

Application Architecture ◽

Context Detection

Authors describe an application for solving video context detection problem. Application architecture use state-of-the-art deap learning TensorFlow framework together with the computer vision library OpenCV in isolated agent environment. The experimental results are shown to demonstrate the effectiveness of developed product.

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Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images

Applied Sciences ◽

10.3390/app11167610 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7610

Author(s):

Khurram Azeem Hashmi ◽

Alain Pagani ◽

Marcus Liwicki ◽

Didier Stricker ◽

Muhammad Zeshan Afzal

Keyword(s):

Computer Vision ◽

Relative Error ◽

State Of The Art ◽

Error Reduction ◽

Detection Accuracy ◽

Document Images ◽

Backbone Network ◽

Mathematical Formulas ◽

Composite Connections ◽

Areas Of Interest

This paper presents a novel architecture for detecting mathematical formulas in document images, which is an important step for reliable information extraction in several domains. Recently, Cascade Mask R-CNN networks have been introduced to solve object detection in computer vision. In this paper, we suggest a couple of modifications to the existing Cascade Mask R-CNN architecture: First, the proposed network uses deformable convolutions instead of conventional convolutions in the backbone network to spot areas of interest better. Second, it uses a dual backbone of ResNeXt-101, having composite connections at the parallel stages. Finally, our proposed network is end-to-end trainable. We evaluate the proposed approach on the ICDAR-2017 POD and Marmot datasets. The proposed approach demonstrates state-of-the-art performance on ICDAR-2017 POD at a higher IoU threshold with an f1-score of 0.917, reducing the relative error by 7.8%. Moreover, we accomplished correct detection accuracy of 81.3% on embedded formulas on the Marmot dataset, which results in a relative error reduction of 30%.

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State-of-the-Art Analysis of Modern Drowsiness Detection Algorithms Based on Computer Vision

2021 29th Conference of Open Innovations Association (FRUCT) ◽

10.23919/fruct52173.2021.9435480 ◽

2021 ◽

Author(s):

Fudail Hasan ◽

Alexey Kashevnik

Keyword(s):

Computer Vision ◽

State Of The Art ◽

Drowsiness Detection ◽

Detection Algorithms ◽

Art Analysis

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All-in-One Framework for Detection, Unpacking, and Verification for Malware Analysis

Security and Communication Networks ◽

10.1155/2019/5278137 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Mi-Jung Choi ◽

Jiwon Bang ◽

Jongwook Kim ◽

Hajin Kim ◽

Yang-Sae Moon

Keyword(s):

Detection Method ◽

State Of The Art ◽

Experimental Results ◽

Detection Accuracy ◽

Malware Analysis ◽

Integrated Framework ◽

Detection Techniques ◽

Verification Algorithm ◽

Extensive Analysis ◽

Hybrid Detection

Packing is the most common analysis avoidance technique for hiding malware. Also, packing can make it harder for the security researcher to identify the behaviour of malware and increase the analysis time. In order to analyze the packed malware, we need to perform unpacking first to release the packing. In this paper, we focus on unpacking and its related technologies to analyze the packed malware. Through extensive analysis on previous unpacking studies, we pay attention to four important drawbacks: no phase integration, no detection combination, no real-restoration, and no unpacking verification. To resolve these four drawbacks, in this paper, we present an all-in-one structure of the unpacking system that performs packing detection, unpacking (i.e., restoration), and verification phases in an integrated framework. For this, we first greatly increase the packing detection accuracy in the detection phase by combining four existing and new packing detection techniques. We then improve the unpacking phase by using the state-of-the-art static and dynamic unpacking techniques. We also present a verification algorithm evaluating the accuracy of unpacking results. Experimental results show that the proposed all-in-one unpacking system performs all of the three phases well in an integrated framework. In particular, the proposed hybrid detection method is superior to the existing methods, and the system performs unpacking very well up to 100% of restoration accuracy for most of the files except for a few packers.

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Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device

Wireless Communications and Mobile Computing ◽

10.1155/2020/8870649 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Zhibin Cheng ◽

Fuquan Zhang

Keyword(s):

Computer Vision ◽

Loss Function ◽

Mobile Device ◽

Detection Method ◽

State Of The Art ◽

Attention Mechanism ◽

Detection Accuracy ◽

Detection Algorithms ◽

End To End ◽

Detection Speed

In this paper, a novel flower detection application anchor-based method is proposed, which is combined with an attention mechanism to detect the flowers in a smart garden in AIoT more accurately and fast. While many researchers have paid much attention to the flower classification in existing studies, the issue of flower detection has been largely overlooked. The problem we have outlined deals largely with the study of a new design and application of flower detection. Firstly, a new end-to-end flower detection anchor-based method is inserted into the architecture of the network to make it more precious and fast and the loss function and attention mechanism are introduced into our model to suppress unimportant features. Secondly, our flower detection algorithms can be integrated into the mobile device. It is revealed that our flower detection method is very considerable through a series of investigations carried out. The detection accuracy of our method is similar to that of the state-of-the-art, and the detection speed is faster at the same time. It makes a major contribution to flower detection in computer vision.

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Driver Drowsiness Detection using MATLAB

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a1991.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 843-844

Keyword(s):

Image Processing ◽

Computer Vision ◽

Detection System ◽

Binary Image ◽

Detection Function ◽

Image Processing Techniques ◽

Drowsiness Detection ◽

Driver Drowsiness ◽

Face Features ◽

Processing Techniques

Drowsiness is major cause of accidents. So, this drowsiness detection system alerts the drowsy drivers in order to reduce the risk of potential accidents. The proposed system uses computer vision and image processing technology of MATLAB for detecting the drowsiness. MATLAB detects if eyes are closed or open using various image processing techniques performed using Viola-Jones face features detecting algorithm and skin y,cb,cr values detection function ,converting image into a binary image which was further employed to extract eye characteristics, and its closing frequency, determining drowsiness.

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