Vehicle classification system using viola Jones and multi-layer perceptron

The automatic vehicle classification system has emerged as an important field of study in image processing and machine vision technologies' implementation because of its variety of applications. Despite many alternative solutions for the classification issue, the vision-based approaches remain the dominant solutions due to their ability to provide a larger number of parameters than other approaches. To date, several approaches with various methods have been implemented to classify vehicles. The fully automatic classification systems constitute a huge barrier for unmanned applications and advanced technologies. This project presents software for a vision-based vehicle classifier using multiple Viola-Jones detectors, moment invariants features, and a multi-layer perceptron neural network to distinguish between different classes. The results obtained in this project show the system’s ability to detect and locate vehicles perfectly in real time via live camera input

Real-time Detection of Vehicles Using the Haar-like Features and Artificial Neuron Networks

AbstractIn this document, a vehicle detection system is presented. This system is based on two algorithms, a descriptor of the image type haar-like, and a classifier type artificial neuron networks. In order to ensure rapidity in the calculation extracts features by the descriptor the concept of the integral image is used for the representation of the image. The learning of the system is performed on a set of positive images (vehicles) and negative images (non-vehicle), and the test is done on another set of scenes (positive or negative). To address the performance of the proposed system by varying one element among the determining parameters which is the number of neurons in the hidden layer; the results obtained have shown that the proposed system is a fast and robust vehicle detector

Designing a Visual Front End in Audio-Visual Automatic Speech Recognition System

Audio-visual automatic speech recognition (AVASR) is a speech recognition technique integrating audio and video signals as input. Traditional audio-only speech recognition system only uses acoustic information from an audio source. However the recognition performance degrades significantly in acoustically noisy environments. It has been shown that visual information also can be used to identify speech. To improve the speech recognition performance, audio-visual automatic speech recognition has been studied. In this paper, we focus on the design of the visual front end of an AVASR system, which mainly consists of face detection and lip localization. The front end is built upon the AVICAR database that was recorded in moving vehicles. Therefore, diverse lighting conditions and poor quality of imagery are the problems we must overcome. We first propose the use of the Viola-Jones face detection algorithm that can process images rapidly with high detection accuracy. When the algorithm is applied to the AVICAR database, we reach an accuracy of 89% face detection rate. By separately detecting and integrating the detection results from all different color channels, we further improve the detection accuracy to 95%. To reliably localize the lips, three algorithms are studied and compared: the Gabor filter algorithm, the lip enhancement algorithm, and the modified Viola-Jones algorithm for lip features. Finally, to increase detection rate, a modified Viola-Jones algorithm and lip enhancement algorithms are cascaded based on the results of three lip localization methods. Overall, the front end achieves an accuracy of 90% for lip localization

A novel infrared video surveillance system using deep learning based techniques

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.This paper presents a new, practical infrared video based surveillance system, consisting of a resolution-enhanced, automatic target detection/recognition (ATD/R) system that is widely applicable in civilian and military applications. To deal with the issue of small numbers of pixel on target in the developed ATD/R system, as are encountered in long range imagery, a super-resolution method is employed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. To tackle the challenge of detecting extremely low-resolution targets, we train a sophisticated and powerful convolutional neural network (CNN) based faster-RCNN using long wave infrared imagery datasets that were prepared and marked in-house. The system was tested under different weather conditions, using two datasets featuring target types comprising pedestrians and 6 different types of ground vehicles. The developed ATD/R system can detect extremely low-resolution targets with superior performance by effectively addressing the low small number of pixels on target, encountered in long range applications. A comparison with traditional methods confirms this superiority both qualitatively and quantitativelyThis work was funded by Thales UK, the Centre of Excellence for Sensor and Imaging System (CENSIS), and the Scottish Funding Council under the project “AALART. Thales-Challenge Low-pixel Automatic Target Detection and Recognition (ATD/ATR)”, ref. CAF-0036. Thanks are also given to the Digital Health and Care Institute (DHI, project Smartcough-MacMasters), which partially supported Mr. Monge-Alvarez’s contribution, and to the Royal Society of Edinburgh and National Science Foundation of China for the funding associated to the project “Flood Detection and Monitoring using Hyperspectral Remote Sensing from Unmanned Aerial Vehicles”, which partially covered Dr. Casaseca-de-la-Higuera’s, Dr. Luo’s, and Prof. Wang’s contribution. Dr. Casaseca-de-la-Higuera would also like to acknowledge the Royal Society of Edinburgh for the funding associated to project “HIVE”

Surround-View Vision-based 3D Detection for Autonomous Driving: A Survey

Vision-based 3D Detection task is fundamental task for the perception of an autonomous driving system, which has peaked interest amongst many researchers and autonomous driving engineers. However achieving a rather good 3D BEV (Bird's Eye View) performance is not an easy task using 2D sensor input-data with cameras. In this paper we provide a literature survey for the existing Vision Based 3D detection methods, focused on autonomous driving. We have made detailed analysis of over $60$ papers leveraging Vision BEV detections approaches and highlighted different sub-groups for detailed understanding of common trends. Moreover, we have highlighted how the literature and industry trend have moved towards surround-view image based methods and note down thoughts on what special cases this method addresses. In conclusion, we provoke thoughts of 3D Vision techniques for future research based on shortcomings of the current techniques including the direction of collaborative perception

Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm

Accuracy in detecting a moving object is critical to autonomous driving or advanced driver assistance systems (ADAS). By including the object classification from multiple sensor detections, the model of the object or environment can be identified more accurately. The critical parameters involved in improving the accuracy are the size and the speed of the moving object. All sensor data are to be used in defining a composite object representation so that it could be used for the class information in the core object’s description. This composite data can then be used by a deep learning network for complete perception fusion in order to solve the detection and tracking of moving objects problem. Camera image data from subsequent frames along the time axis in conjunction with the speed and size of the object will further contribute in developing better recognition algorithms. In this paper, we present preliminary results using only camera images for detecting various objects using deep learning network, as a first step toward multi-sensor fusion algorithm development. The simulation experiments based on camera images show encouraging results where the proposed deep learning network based detection algorithm was able to detect various objects with certain degree of confidence. A laboratory experimental setup is being commissioned where three different types of sensors, a digital camera with 8 megapixel resolution, a LIDAR with 40m range, and ultrasonic distance transducer sensors will be used for multi-sensor fusion to identify the object in real-time

Vehicle Classification For Automatic Traffic Density Estimation

Automatic traffic light control at intersection has recently become one of the most active research areas related to the development of intelligent transportation systems (ITS). Due to the massive growth in urbanization and traffic congestion, intelligent vision based traffic light controller is needed to reduce the traffi c delay and travel time especially in developing countries as the current automatic time based control is not realistic while sensor-based tra ffic light controller is not reliable in developing countries. Vision based traffi c light controller depends mainly on traffic congestion estimation at cross roads, because the main road junctions of a city are these roads where most of the road-beds are lost. Most of the previous studies related to this topic do not take unattended vehicles into consideration when estimating the tra ffic density or traffi c flow. In this study we would like to improve the performance of vision based traffi c light control by detecting stationary and unattended vehicles to give them higher weights, using image processing and pattern recognition techniques for much e ffective and e ffecient tra ffic congestion estimation

Detection of Driver Drowsiness and Distraction Using Computer Vision and Machine Learning Approaches

Drowsiness and distracted driving are leading factor in most car crashes and near-crashes. This research study explores and investigates the applications of both conventional computer vision and deep learning approaches for the detection of drowsiness and distraction in drivers. In the first part of this MPhil research study conventional computer vision approaches was studied to develop a robust drowsiness and distraction system based on yawning detection, head pose detection and eye blinking detection. These algorithms were implemented by using existing human crafted features. Experiments were performed for the detection and classification with small image datasets to evaluate and measure the performance of system. It was observed that the use of human crafted features together with a robust classifier such as SVM gives better performance in comparison to previous approaches. Though, the results were satisfactorily, there are many drawbacks and challenges associated with conventional computer vision approaches, such as definition and extraction of human crafted features, thus making these conventional algorithms to be subjective in nature and less adaptive in practice. In contrast, deep learning approaches automates the feature selection process and can be trained to learn the most discriminative features without any input from human. In the second half of this research study, the use of deep learning approaches for the detection of distracted driving was investigated. It was observed that one of the advantages of the applied methodology and technique for distraction detection includes and illustrates the contribution of CNN enhancement to a better pattern recognition accuracy and its ability to learn features from various regions of a human body simultaneously. The comparison of the performance of four convolutional deep net architectures (AlexNet, ResNet, MobileNet and NASNet) was carried out, investigated triplet training and explored the impact of combining a support vector classifier (SVC) with a trained deep net. The images used in our experiments with the deep nets are from the State Farm Distracted Driver Detection dataset hosted on Kaggle, each of which captures the entire body of a driver. The best results were obtained with the NASNet trained using triplet loss and combined with an SVC. It was observed that one of the advantages of deep learning approaches are their ability to learn discriminative features from various regions of a human body simultaneously. The ability has enabled deep learning approaches to reach accuracy at human level.