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Traffic monitoring and vehicle counting systems that use surveillance cameras employ several computer vision techniques, one of which is object tracking, which approximates the trajectory of the vehicle throughout the scene. However, a major challenge in processing videos from network camera feeds is the irregular and low frame rates, affecting the performance of object tracking. In this paper, we present a concurrent implementation framework intended to increase the input network video frame rate.

This paper presents a vision-based approach to lane identification and estimation of service rate, arrival rate, and queue saturation. The method is based on analyzing object trajectories produced. Experiments are demonstrated by applying the proposed method to different traffic scenarios: light, moderate, and heavy. The accuracy of the test is examined by comparing the queue analysis results against the ground truth. Results show that the approach is able to yield satisfactory results when the vehicle movement stays within the lane. However, the error increases when vehicle movement overlaps or switches lanes. In conclusion, the algorithm works to identify the lane membership of trajectories under different conditions. The proposed method could also be used to automate the estimation of traffic congestion levels at sections covered by surveillance cameras.

Airline industry is a major global leader in aviation which has made the world an alluring smaller place with an enormous level of mobility and accessibility. It even provides service to almost every other sector, however, it does not receive significant attention, despite its importance. Moreover, its service quality is an aggregate of different interactions between the customers and airline companies. This drove them to implement new business model to conform to the new competitive atmosphere in the industry whose operation is taking place in a completely globalized environment. Thus, the use of chatbots is one of the avenues to meet this end. However, there is a dearth of standardized and validated instrument that best fit to evaluate the use of chatbots in the airline industry based on the combined set of constructs as identified in the conceptual framework. Thus, this study aims to develop and validate an instrument that will evaluate the adoption of chatbots in a service industry, like the airline industry, as well as its non-adoption. Based on the reliability and internal consistency evaluation, only one item was deleted (PIIT construct) since it has the lowest item-test correlation that caused the reliability coefficient to be less than the suggested value of 0.70. All statements in each of the constructs had positive signs, thus, were not reversely worded. Lastly, all the scale reliability coefficient or the overall alpha values are way higher than the suggested value, which means that the internal consistency is either acceptable or highly acceptable.

In agriculture, camouflage, i.e. fruits are occluded due to high color similarity to its environment, is one of the problems of fruit detection and counting for yield estimation. In this paper, we developed a system that automatically detects and counts fruits from images in an unstructured environment with heavy occlusion and high color similarity. We built two datasets by capturing the natural settings of the fruit and fine-tuned various deep convolutional neural networks with region proposal network for a very challenging task of detecting heavily occluded fruits with high color similarity against the background. The evaluation results show an average precision of 0.922 for chili pepper dataset and 0.966 for bell pepper dataset. The result of this network can be integrated into an application for efficient management of agricultural farms, thereby contributing to precision agriculture.

This paper describes a system that can distinguish between visually-similar objects, specifically bicycles and motorcycles, successfully from the vantage point of traffic surveillance cameras. The You Only Look Once (YOLO) is used as the main framework in this research due to its speed performance among various machine learning models and methods evaluated. We built a dataset consisting of motorcycle and bicycle images from different CCTV footage for this project. CCTV footage may vary on the angle of view from the object, image resolution, and ambient environment settings. Using this dataset, we trained YOLOv3-based models, and their performances were compared to the vanilla version of YOLOv3 and other pre-trained models. Four (4) models were trained and compared; the best-performing model is shown to be associated with a dataset with properly labeled data (i.e., marking every instance of the object of interest) and having the most number of instances in the training and testing set.Introduction.

Algorithms that perform crowd estimation are dependent on crowd levels. The two approaches to crowd estimation discussed are the model-based and texture-based approaches. The aim of this work is to determine the precision, recall and F-measure of the two algorithms, Histogram of Oriented Gradients (HOG) with Support Vector Machines (SVM) and Region-Specific HOG, for estimating the number of people in high and low crowd levels, respectively, in an indoor area installed with a surveillance camera, while considering the camera’s position and its field of view.

A radiograph is a visualization aid that physicians use in identifying lung abnormalities. Although digitized x-ray images are available, diagnosis by a medical expert through pattern recognition is done manually. Thus, this paper presents a system that utilizes machine learning for pattern recognition and classification of six lung conditions. Classified into two categories, namely histogram-based (normal, pleural effusion, and pneumothorax) and statistics-based (cardiomegaly, hyperaeration, and possible lung nodules). Using preprocessing and feature extraction techniques, the designed system achieves an accuracy rate of 92.59% for the histogram-based lung conditions using Sequential Minimal Optimization (SMO) and 67.22% for the statistics-based lung conditions using logic operations.

Performance analysis for basketball development programs are based on the athletes’ movement patterns, playing position on an area, and ball acquisition. The objective of the system is to automate the performance analysis by providing raw statistical data based on the player’s behaviors produced through tracking the player inside the basketball court. Using visual features described by SURF, unoccluded players are localized using a tracking by detection approach with an observed accuracy of 40%. Player positions are also adjusted to compensate for distortions, which improves player localization by 0.11%, on average.

Multiple-image super-resolution (MISR) attempts to recover a high-resolution (HR) image from a set of lowresolution (LR) images. In this paper, we present a mobile MISR tailored to work for a wide range of mobile devices. Our technique aims to address misalignment issues from a previous work and further enhance the quality of HR images produced. The proposed architecture is used to implement a prototype application that is freely available at Google Play Store, titled Eagle-Eye HD Camera. The system is divided into the following modules: Input Module, Edge Detection Module, Image Selection Module, Image Alignment Module, Alignment Selection Module and Image Fusion Module. We assessed the quality of HR images produced by our mobile MISR, through an online survey, as well as compare it with other related SR works. Performance time was also measured. A total of 114 respondents have participated in the survey, where majority of respondents preferred our approach. Our approach is observed to be comparable with other SR works in terms of visual quality and performance time, and guaranteed to work in a mobile environment.

The sugarcane stalk and foliage colors are strong indicators of maturity. While outdoor conditions affect the lighting of the captured sugarcane image, external factors like shadows and over exposure can easily be neglected in the Hue Saturation Value (HSV) color space. Through a series of experimentations in the HSV color space, the proponents found out that there is a significant shifting of Hue and Saturation values as sugarcane crops mature. The Hue and Saturation frequencies of both matured and not-matured sugarcanes were used as data for the maturity detection comparison module which utilizes RandomForest algorithm with 90.16% accuracy.

A “triaxial velocity sensor” consists of three uniaxial velocity sensors, which are nominally identical, orthogonally oriented among themselves, and co-centered at one point in space. A triaxial velocity sensor measures the acoustic particle velocity vector, by its three Cartesian components, individually component-by-component, thereby offering azimuth-elevation two-dimensional spatial directivity, despite the physical compactness that comes with the collocation of its three components. This sensing system's azimuth-elevation beam-pattern has been much analyzed in the open literature, but only for an idealized case of the three uniaxial velocity sensors being exactly identical in gain. If this nominal identity is violated among the three uniaxial velocity sensors, as may occur in practical hardware, what would happen to the corresponding “spatial matched filter” beam-pattern's peak direction? How would this effective peak direction deviate from the nominal “look direction”? This paper, by modeling each uniaxial velocity sensor's gain as stochastic, derives this deviation's statistical mean and variance, analytically in closed mathematical forms. This analytical derivation is verified by Monte Carlo simulations.