An improved normalized gain-based score normalization technique for spoof detection algorithm

A spoof detection algorithm supports the speaker verification system to examine the false claims by an imposter through careful analysis of input test speech. The scores are employed to categorize the genuine and spoofed samples effectively. Under the mismatch conditions, the false acceptance ratio increases and can be reduced by appropriate score normalization techniques. In this article, we are using the normalized Discounted Cumulative Gain (nDCG) norm derived from ranking the speaker’s log-likelihood scores. The proposed scoring technique smoothens the decaying process due to logarithm with an added advantage from the ranking. The baseline spoof detection system employs Constant Q-Cepstral Co-efficient (CQCC) as the base features with a Gaussian Mixture Model (GMM) based classifier. The scores are computed using the ASVspoof 2019 dataset for normalized and without normalization conditions. The baseline techniques including the Zero normalization (Z-norm) and Test normalization (T-norm) are also considered. The proposed technique is found to perform better in terms of improved Equal Error Rate (EER) of 0.35 as against 0.43 for baseline system (no normalization) wrt to synthetic attacks using development data. Similarly, improvements are seen in the case of replay attack with EER of 7.83 for nDCG-norm and 9.87 with no normalization (no-norm). Furthermore, the tandem-Detection Cost Function (t-DCF) scores for synthetic attack are 0.015 for no-norm and 0.010 for proposed normalization. Additionally, for the replay attack the t-DCF scores are 0.195 for no-norm and 0.17 proposed normalization. The system performance is satisfactory when evaluated using evaluation data with EER of 8.96 for nDCG-norm as against 9.57 with no-norm for synthetic attacks while the EER of 9.79 for nDCG-norm as against 11.04 with no-norm for replay attacks. Supporting the EER, the t-DCF for nDCG-norm is 0.1989 and for no-norm is 0.2636 for synthetic attacks; while in case of replay attacks, the t-DCF is 0.2284 for the nDCG-norm and 0.2454 for no-norm. The proposed scoring technique is found to increase spoof detection accuracy and overall accuracy of speaker verification system

Development of a license plate recognition system for a non-ideal environment

A new algorithm for license plate character recognition system is proposed on the basis of Signature analysis properties and features extraction. Signature analysis has been used to locate license plate region and its properties can be further utilised in supporting and affirming the license plate character recognition. This paper presents the implementation of Signature Analysis combined with Features Extraction to form feature vector for each character with a length of 56. Implementation of these two methods is used in tracking of vehicle’s automatic license plate recognition system (ALPR). The developed ALPR comprises of three phase. The recognition stage utilised the vector to be trained in a simple multi-layer feed-forward back-propagation Neural Network with 56 inputs and 34 neurons in its output layer. The network is trained with both ideal and noisy characters. The results obtained show that the proposed system is capable to recognise both ideal and non-ideal license plate characters. The system also capable to tackle the common character misclassification problems due to similarity in characters

On-shore plastic waste detection with YOLOv5 and RGB-near-infrared fusion: a state-of-the-art solution for accurate and efficient environmental monitoring

Plastic waste is a growing environmental concern that poses a significant threat to onshore ecosystems, human health, and wildlife. The accumulation of plastic waste in oceans has reached a staggering estimate of over eight million tons annually, leading to hazardous outcomes in marine life and the food chain. Plastic waste is prevalent in urban areas, posing risks to animals that may ingest it or become entangled in it, and negatively impacting the economy and tourism industry. Effective plastic waste management requires a comprehensive approach that includes reducing consumption, promoting recycling, and developing innovative technologies such as automated plastic detection systems. The development of accurate and efficient plastic detection methods is therefore essential for effective waste management. To address this challenge, machine learning techniques such as the YOLOv5 model have emerged as promising tools for developing automated plastic detection systems. Furthermore, there is a need to study both visible light (RGB) and near-infrared (RGNIR) as part of plastic waste detection due to the unique properties of plastic waste in different environmental settings. To this end, two plastic waste datasets, comprising RGB and RGNIR images, were utilized to train the proposed model, YOLOv5m. The performance of the model was then evaluated using a 10-fold cross-validation method on both datasets. The experiment was extended by adding background images into the training dataset to reduce false positives. An additional experiment was carried out to fuse both the RGB and RGNIR datasets. A performance-metric score called the Weighted Metric Score (WMS) was proposed, where the WMS equaled the sum of the mean average precision at the intersection over union (IoU) threshold of 0.5 ([email protected]) × 0.1 and the mean average precision averaged over different IoU thresholds ranging from 0.5 to 0.95 ([email protected]:0.95) × 0.9. In addition, a 10-fold cross-validation procedure was implemented. Based on the results, the proposed model achieved the best performance using the fusion of the RGB and RGNIR datasets when evaluated on the testing dataset with a mean of [email protected], [email protected]:0.95, and a WMS of 92.96% ± 2.63%, 69.47% ± 3.11%, and 71.82% ± 3.04%, respectively. These findings indicate that utilizing both normal visible light and the near-infrared spectrum as feature representations in machine learning could lead to improved performance in plastic waste detection. This opens new opportunities in the development of automated plastic detection systems for use in fields such as automation, environmental management, and resource management

Multiple vehicles license plate tracking and recognition via isotropic dilation

In this paper, a new algorithm for Automatic License Plate Localisation and Recognition (ALPR) is proposed on the basis of isotropic dilation that can be achieved using the binary image Euclidean distance transform. In a blob analysis problem, any two Region of Interest (RoIs) that is discontinuous are typically treated as separate blobs. However, the proposed algorithm combine with Connected Component Analysis (CCA) are coded to seek for RoI within a certain distance of other RoI to be treated as non-unique. This paper investigates the design and implementation of several pre-processing techniques and isotropic dilation algorithm to classify moving vehicles with different backgrounds and varying angles. A multi-layer feed-forward back-propagation Neural Network is used to train the segmented and refined characters. The results obtained can be used for implementation in the vehicle parking management system

Graph-Based Image Segmentation Using K-Means Clustering and Normalised Cuts

Image segmentation with low computational burden has been highly regarded as important goal for researchers. Various image segmentation methods are widely discussed and more noble segmentation methods are expected to be developed when there is rapid demand from the emerging machine vision field. One of the popular image segmentation methods is by using normalised cuts algorithm. It is unfavourable for a high resolution image to have its resolution reduced as high detail information is not fully made used when critical objects with weak edges is coarsened undesirably after its resolution reduced. Thus, a graph-based image segmentation method done in multistage manner is proposed here. In this paper, an experimental study based on the method is conducted. This study shows an alternative approach on the segmentation method using k-means clustering and normalised cuts in multistage manner

Modeling of vehicle trajectory clustering based on LCSS for traffic pattern extraction

The emerging of the intelligent transportation system especially in the research area of traffic surveillance and solving traffic congestions, become notably crucial for traffic operators in the aim of achieving efficient vehicle flow. However, behavioural manoeuvres that describe the pattern of vehicles movements and change of the vehicle flow are not sufficiently modeled based on the conventional inductive-loop traffic sensors. These behavioural manoeuvres are useful for interpreting the indepth study of traffic pattern in a traffic network. Hence, with the advancement of the available vehicle tracking system, vehicle trajectory dataset is selected as suitable candidate input for the traffic pattern extraction. The implementation of k-means and fuzzy c-means (FCM) clustering algorithm for vehicle flow analyzing task is served as focus in this paper. Similarity function based on Longest Common Subsequence (LCSS) is implemented to measure the similarity among the trajectories before clustering is performed. Rand Index (RI) is computed to evaluate the clustering performance of two sets trajectories with two different traffic scenes by comparing the simulated clustering result with the ground-truth result

Modeling of vehicle trajectory using K-means and fuzzy C-means clustering

The implementation of information technology in transportation system is becoming a leading trend nowadays due to alleviating the traffic problems such as traffic congestions and accidents are targeted as primary concerns by the traffic operators. Thus, monitoring the traffic scene serves as basis for the traffic operators especially at traffic intersection. Extracted traffic data from the monitoring system is often massive which requires efforts in searching for significant patterns in it. These patterns describe the vehicle movements are useful for observation of any abnormal behavior that leads to traffic conflicts. However, it will be a tremendous work for traffic operators to observe the vehicle flows manually where thousands of vehicles may travel through an intersection. Hence, the clustering of vehicle trajectory dataset for similar patterns identification is implemented with k-means and fuzzy c-means (FCM) clustering algorithm. As these clustering algorithms require the number of clusters as input parameter of the algorithms, the study of number of clusters for the clustering is served as focus in this paper. The evaluation of clustering performance with different input parameter of number of clusters is discussed in this paper

Computing non-contactable drowsiness monitoring system with mobile machine vision

This project proposes a human facial features detection based on color segmentation via skin color and Viola- Jones algorithm for real time application. YCbCr color space is used to detect the presence of skin in an image where the image is normalized, and luminance is removed to increase face detection accuracy. The second method, Viola-Jones which use Haar feature to detect facial feature such as face and eye also developed and tested. To perform in real time detection, CamShift algorithm and template matching are used to track face and eyes sequentially in Android platform. Then, the real time detection and tracking are evaluated to assess its performance. Finally, the algorithm is applied to drowsiness detection using PERCLOS

On-Shore Plastic Waste Detection with YOLOv5 and RGB-Near-Infrared Fusion: A State-of-the-Art Solution for Accurate and Efficient Environmental Monitoring

Plastic waste is a growing environmental concern that poses a significant threat to onshore ecosystems, human health, and wildlife. The accumulation of plastic waste in oceans has reached a staggering estimate of over eight million tons annually, leading to hazardous outcomes in marine life and the food chain. Plastic waste is prevalent in urban areas, posing risks to animals that may ingest it or become entangled in it, and negatively impacting the economy and tourism industry. Effective plastic waste management requires a comprehensive approach that includes reducing consumption, promoting recycling, and developing innovative technologies such as automated plastic detection systems. The development of accurate and efficient plastic detection methods is therefore essential for effective waste management. To address this challenge, machine learning techniques such as the YOLOv5 model have emerged as promising tools for developing automated plastic detection systems. Furthermore, there is a need to study both visible light (RGB) and near-infrared (RGNIR) as part of plastic waste detection due to the unique properties of plastic waste in different environmental settings. To this end, two plastic waste datasets, comprising RGB and RGNIR images, were utilized to train the proposed model, YOLOv5m. The performance of the model was then evaluated using a 10-fold cross-validation method on both datasets. The experiment was extended by adding background images into the training dataset to reduce false positives. An additional experiment was carried out to fuse both the RGB and RGNIR datasets. A performance-metric score called the Weighted Metric Score (WMS) was proposed, where the WMS equaled the sum of the mean average precision at the intersection over union (IoU) threshold of 0.5 ([email protected]) × 0.1 and the mean average precision averaged over different IoU thresholds ranging from 0.5 to 0.95 ([email protected]:0.95) × 0.9. In addition, a 10-fold cross-validation procedure was implemented. Based on the results, the proposed model achieved the best performance using the fusion of the RGB and RGNIR datasets when evaluated on the testing dataset with a mean of [email protected], [email protected]:0.95, and a WMS of 92.96% ± 2.63%, 69.47% ± 3.11%, and 71.82% ± 3.04%, respectively. These findings indicate that utilizing both normal visible light and the near-infrared spectrum as feature representations in machine learning could lead to improved performance in plastic waste detection. This opens new opportunities in the development of automated plastic detection systems for use in fields such as automation, environmental management, and resource management