This research proposes to automate the plate recognition process by installing an IP camera on a road and analyzing the video-feed to capture the vehicles along that road. The contours of the characters in a given plate image are detected, violated and isolated from the parent image. This results to segmented characters. Each of the characters are identified using a k nearest neighbors (kNN) algorithm. The kNN algorithm was trained using different sets of training data containing 36 characters each. The algorithm was tested on the previously segmented characters. The simulations show that an accuracy of 87.43% was achieved for the plate recognition algorithm using kNN at k = 1. Compared against existing character recognition techniques such as artificial neural networks (ANN), the difference in the accuracy is minimal. Moreover, the average processing time was 0.034 s.

This paper presents a machine vision algorithm to detect traffic violations specifically swerving and blocking the pedestrian lane. The proposed solution consists of background difference method, and focuses on the genetic algorithm of the system to detect these violations. The general process is as follows: a capture picture is to be subtracted first by the reference image, then the genetic algorithm is run to find the violator, and finally a display is outputted with the corresponding type of violation. The machine vision traffic violation detection system was found to have an average convergence of about 8 iterations, within an average of less than 300 generations. These results show that the algorithm is well-suited for real time implementation in traffic detection system. Provided the system inputs were captured photos from a CCTV camera, whereas the outputs were cropped pictures of the car that was detected to have such violations mentioned earlier.

Implementing computer vision on traffic scenarios are one of the most widely sought area in the field of vision research. In dealing with the surveillance in traffic scenarios, every vehicle in the scene must be observed which results to problem arising from instances whenever the traffic density in an area is high due to occlusion caused by the large number of vehicles being observed. Thus, this paper proposes a vehicle detection and tracking algorithm whose main purpose is to detect and track vehicles entering an intersection and track them robustly in real-time. The algorithm which was used is a blob analysis and tracking based on a mean-shift kernel. The blob approach acts as the main tracking and will use the mean-shift in the event of blob merging or occlusion. In this paper, the proposed tracking method is tested using a CCTV camera on an intersection with high traffic density to illustrate the capability of solving occlusion and observe the robustness of the algorithm in the scene. The results show that the proposed system successfully tracks the vehicles during and after occlusion with other vehicles or other types of objects in the scene.

Developing countries face the problem of crowded and congested roads because of inefficient implementation of traffic rules. Motorists ignore the rules because they are not apprehended and can get away easily. This paper proposes an intelligent traffic system that is able to automatically detect and apprehend traffic violators, specifically motorists who either swerve or block the pedestrian lane. The system is designed by integrating three processes: violation detection, plate localization and plate recognition. The violation detection and plate localization were realized using genetic algorithm while the plate recognition process was performed using an artificial neural network. The recognition of the plate number is highly dependent on the position of the detected vehicle with respect to the camera. Thus, the recognized plate number will only be supplementary information about the violator; the physical attributes of the vehicle which is captured by the violation detection process will be the main information on the violator. Based on the results of 48 images tested, the overall system was able to detect the mentioned violations and to identify the plate number of the vehicles that were detected as traffic violators, with an average accuracy of 90.67%, and program runtime of 1.34 seconds.

The coconut is one of the most useful trees in the world. Its fruit, with the scientific name cocos nucifera, is one of the major agricultural products of the Philippines. The coconut fruit depending on its maturity is used as a food or as a beverage. There are three stages of maturity namely: malauhog, malakanin, and malakatad. The classification into each stage may be based on the color and hardness of its shell so as the amount and tenderness of its meat. To categorize maturity stage, this paper uses fuzzy logic with color and sound as fuzzy inputs. Image color analysis for determining the percentage brown in the shell. Sound spectral analysis for relating the shell hardness and meat condition. Fuzzy inference system for evaluating the relationship of sound and color with the maturity of a coconut fruit. This study is able to simulate coconut fruit maturity classification system using a fuzzy logic approach.

This paper proposes a vehicle plate optical character recognition method using scale invariant feature transform integrated with image segmentation and fuzzy logic. Image segmentation separates every character in a plate area to get the features of every character obtained. Scale Invariant Feature Transform or SIFT on the other hand, allows the extraction of every feature of each character obtained from the plate. Fuzzy logic analyzes the features obtained from the SIFT algorithm which is proposed to detect the characters correctly. This program used MATLAB to determine the performance of the algorithm. Using the proposed algorithm, it was shown how the algorithm was effective on extracting plate character features as well as recognizing the characters in a given image. Results show that the algorithm has an accuracy of 90.75% and now ready to use for other implementation. This can be incorporated to present optical character recognition system and test its validity and accuracy for practical purposes.