Vanishing Point and Gabor Feature Based Multi-resolution On-Road Vehicle Detection

With the rapid growth of the global economy, the global car ownership is also increasing year by year, which has caused a series of problems, the most prominent of which is traffic congestion and traffic accidents. In order to solve the traffic problem, all countries are actively studying the intelligent transportation system, and one of the important research contents of the intelligent transportation system is vehicle detection. Vehicle detection based on vision is to capture vehicle images in the driving environment through a camera, and then use computer vision recognition technology for vehicle detection and recognition. Although computer vision recognition technology has made great progress, how to improve the detection accuracy of the image to be detected is still an important content of visual recognition technology research. Intelligent vehicle visual robust detection and identification of methods of research to reduce the growing incidence of traffic accidents, improve the existing road traffic safety and transportation efficiency, alleviate the degree of driver fatigue problem are of great significance. This paper considers the intelligent vehicle environmental awareness of the key technology to the goal of robust detection and recognition based on machine vision problems for further research. The particle filter is used to extract the local energy of the image to realize the fast segmentation of the region of interest (ROI). In order to further verify the ROI, a measure learning method based on multi-core embedding is proposed, and the semantic classification of ROI is realized by integrating the color, shape and geometric features of ROI. Experimental results show that the algorithm can effectively eliminate false sexy ROI interest, and the algorithm is robust to complex background, illumination changes, perspective changes and other conditions.

Research on Vehicle Detection and Recognition Technology Based on Artificial Intelligence

Vehicle detection and tracking plays a major role in military and civilian applications. The accurate detection of multiple vehicles in a complicated traffic environment is too difficult. This process is made more difficult, if there are occlusions between vehicles. The optimal feature selection of a vehicle is also a challenging task. These issues are focused in the existing work namely convolution neural network (CNN) based vehicle detection. However in this work, feature selection is not concentrated and with the lesser feature information classification accuracy is reduced considerably. This is focused and resolved in the proposed research by introducing the method namely Enhanced Convolution neural network with Support Vector Machine (ECNN-SVM) based vehicle detection. In this work initially feature extraction is done. Haar like features are used for this purpose. This has compact representation, capture information from multiple scales, encoded edge and structural information and can be efficient computation. Haar like feature pool is large scale in nature. Enhanced bat optimization is used to select features from this pool. This selection is done by combining sample’s class label and feature value. Confidence level between two thresholds of tracking rectangles is used for classification. Support vector machine (SVM) classifier is combined with local binary pattern to perform this classification. The interference areas between moving objects and vehicles are removed by Enhanced Convolutional Neural Network (ECNN) classifier. The efficiency of the proposed method is computed by using experimental and theoretical analysis.

Vehicle detection plays a vital role in the design of Automatic Driving System (ADS), which has achieved remarkable improvements in recent years. However, vehicle detection in night scenes still has considerable challenges for the reason that the vehicle features are not obvious and are easily affected by complex road lighting or lights from vehicles. In this paper, a high-accuracy vehicle detection algorithm is proposed to detect vehicles in night scenes. Firstly, an improved Generative Adversarial Network (GAN), named Attentive GAN, is used to enhance the vehicle features of nighttime images. Then, with the purpose of achieving a higher detection accuracy, a multiple local regression is employed in the regression branch, which predicts multiple bounding box offsets. An improved Region of Interest (RoI) pooling method is used to get distinguishing features in a classification branch based on Faster Region-based Convolutional Neural Network (R-CNN). Cross entropy loss is introduced to improve the accuracy of classification branch. The proposed method is examined with the proposed dataset, which is composed of the selected nighttime images from BDD-100k dataset (Berkeley Diverse Driving Database, including 100,000 images). Compared with a series of state-of-the-art detectors, the experiments demonstrate that the proposed algorithm can effectively contribute to vehicle detection accuracy in nighttime.

Vehicle detection and classification are important tasks in the automatic traffic monitoring system. The proposed work focuses on vehicle detection and classification. Vehicle detection is carried out using the combination of dense optical flow method and integrated binary projection profile. Inception-ResNet-v2 is used as a feature extraction technique and extracted features are fed to two different classifiers such as Support Vector Machine and Random Forest to classify the vehicle type. The recognition performance of Inception-ResNet-v2 with these classifiers is significantly high and the proposed approach obtained an output accuracy as 99.89% and 98.615% in Support Vector Machine and Random forest respectively.

Automatic vehicle detection from high-resolution remote sensing image is a challenging topic. While there have been some studies on this topic in recent years, a fast and robust approach is yet to be found, especially when facing the scenario of low color contrast. In this paper, a new vehicle detection approach is proposed. First, superpixel-based segmentation is used to identify potential vehicle regions to speed up the detection and improve the accuracy. Then, an image enhancement method is also proposed, which greatly improves the segmentation results. Support vector machine is used to classification with features extracted by HOG descriptor. According to the experiments, by combining with superpixel segmentation and the image enhancement, the speed of the vehicle detection is improved with approximately an order of magnitude. Also, in case of low contrast remote sensing images, the detection accuracy can be also greatly improved, with much less false positives and false negatives.

This paper presents an effective nighttime vehicle detection system that combines a novel bioinspired image enhancement approach with a weighted feature fusion technique. Inspired by the retinal mechanism in natural visual processing, we develop a nighttime image enhancement method by modeling the adaptive feedback from horizontal cells and the center-surround antagonistic receptive fields of bipolar cells. Furthermore, we extract features based on the convolutional neural network, histogram of oriented gradient, and local binary pattern to train the classifiers with support vector machine. These features are fused by combining the score vectors of each feature with the learnt weights. During detection, we generate accurate regions of interest by combining vehicle taillight detection with object proposals. Experimental results demonstrate that the proposed bioinspired image enhancement method contributes well to vehicle detection. Our vehicle detection method demonstrates a 95.95% detection rate at 0.0575 false positives per image and outperforms some state-of-the-art techniques. Our proposed method can deal with various scenes including vehicles of different types and sizes and those with occlusions and in blurred zones. It can also detect vehicles at various locations and multiple vehicles.

In nighttime images, vehicle detection is a challenging task because of low contrast and luminosity. In this article, the authors combine a novel region-of-interest (ROI) extraction approach that fuses vehicle light detection and object proposals together with a nighttime image enhancement approach based on improved multiscale retinex to extract accurate ROIs and enhance images for accurate nighttime vehicle detection. Experimental results demonstrate that the proposed nighttime image enhancement method, score-level multifeature fusion, and the ROI extraction method are all effective for nighttime vehicle detection. But the proposed vehicle detection method demonstrates 93.34 percent detection rate and outperforms other models, detecting blurred and partly occluded vehicles, as well as vehicles in a variety of sizes, numbers, locations, and backgrounds.

This work has the objective of developing a methodology for the classification of regions of interest (ROI) extracted from mammograms into masses and non-masses. To this end, we conducted a comparative study by combining several techniques in the various stages of the problem. We compared several texture-based techniques for the classification of ROIs. This study combines image processing, phylogenetic trees (PT), local binary patterns (LBP), support vector machines (SVM) and the micro-genetic algorithm (GA). The analysis of texture is performed, either through the combination PT/LBP or with grey levels, to compute the taxonomic diversity () and taxonomic distinction (*) indexes extracted from sub-regions (circular, circular crown, internal mask, external mask and the combination of internal and external mask) of an ROI. A GA is used to estimate the best phylogenetic weights. Then, its results are compared with the results achieved with the use of PT only. We also analyse the behaviour of the methodology when using the ROIs with and without enhancement. This enhancement consists of the application of a mean filter and the contrast-limited adaptive histogram equalisation (CLAHE). PTs and SVM were used to perform the selection of features. To evaluate the performance of the methodologies under analysis, we used the following metrics: sensitivity, specificity, accuracy and area under the receiver operating characteristic (ROC) curve (). Sensitivity and specificity measure the efficiency of the classifier at the correct detection of positive (masses) and negative (non-masses) cases, respectively. Accuracy measures the performance of the classification in both cases. The ROC curve is the graphical representation of the pairs (1-specificity, sensitivity). is the area formed by the ROC curve, which equals to 1 in an ideal test. The comparison of the possible combinations for each stage of the study revealed the following results. In the analyses without feature selection, the best results were (1) 100% accuracy and of 0.99 for the combination of PT, LBP and internal masks and (2) 99.5% accuracy and of 0.99 for the combination of GA, LBP and internal masks for the extraction of features. In the analyses with feature selection, the best results were (1) 100% accuracy and of 1.0 for the combination of GA and LBP (feature extraction) with the union of internal and external masks and (2) 98.5% accuracy and of 0.99 for the combination of PT, LBP and the union of internal and external masks.

We propose a method of real-time moving vehicle detection using infrared thermal images. It can detect moving vehicles robustly even for bad environments compared with conventional vehicle detection methods using visible light cameras. It can also measure the size of each vehicle around the clock. The algorithm we propose for this detection is designed for a high-speed processing without complicated calculations and also designed for a real-time vehicle detection by using a general-purpose personal computer. Experimental results of our method by use of infrared thermal traffic images both at daytime with vehicular shadows and at nighttime in darkness show that the vehicle detection accuracy is 100%.

Nighttime vehicle detection is part of an intelligent transportation system for road safety, driving navigation, and surveillance at night. However, previous nighttime vehicle detection methods only deal with a single class or two classes of vehicles. This paper presents an effective system based on cascade feature selection and a coarse-to-fine mechanism for detecting preceding multiclass vehicles at night. First, we train a coarse-level classifier using contrast features. Second, we combine a cascade selection framework with feature mutual correlation, similarity and regions’ overlap to select a set of image regions, where we can extract effective features. Three features, including local binary pattern, histogram of oriented gradients, and four direction features, are extracted from the selected regions for training a fine-level multiclass vehicle classifier. During the detection stage, we utilize a coarse-to-fine mechanism. In the coarse level, a multiscale sliding window is classified by the coarse-level classifier to find regions of interest (ROIs) that are likely to be vehicles. In the fine level, these ROIs are identified by the trained multiclass vehicle classifier. Evaluations on a Hong Kong nighttime multiclass vehicle dataset show that our proposed system successfully detects a car, taxi, bus, and minibus within nighttime images under different scenes. Quantitatively, our proposed system obtains 95.48% detection rate at 0.055 false positives per image, outperforming some state-of-the-art detection approaches including two current nighttime vehicle detection methods, and two deep learning-based methods.

Detecting vehicle turn signals at night is critical for both assistant driving systems and autonomous driving systems. In this paper, we propose a novel method that consists of detection and tracking modules to achieve a high level of robustness. For nighttime vehicle detection, a Nakagami-image-based method is used to locate the regions containing vehicle lights. At the same time, a set of vehicle object proposals is generated using a region proposal network based on convolutional neural network (CNN) feature maps. Then, the light regions and proposals are combined to generate the regions of interest (ROIs) for the further detection. Vehicle candidates are extracted from the ROIs using a softmax classifier with CNN-based features. For the tracking module, we propose a perceptional hashing algorithm to track these vehicle candidates. During the tracking, turn signals are detected by analyzing the continuous intensity variation of the vehicle box sequences. Experimental results for typical sequences show that the proposed method can robustly detect and track a vehicle in front with over 95% accuracy and recognize the turning signals in night scenes with a detection rate of over 90%. The vehicle detection method improves the miss rate of state-of-the-art systems by more than 20%. In addition, the proposed vehicle tracking method outperforms other state-of-the-art systems.

Driver assistance system in most of the present vehicle was using preemptive scheduler based operating system for adaptive cruise control function. Highly predictable and at low cost system can be implemented using Time triggered cooperative scheduler, instead of a full RTOS, RTOS require more calculation time and large memory space which is not available in smaller embedded systems and is very complex and difficult to debug errors. One of the simplest methods is Time Triggered cooperative scheduler for controlling adaptive cruise control in vehicle and will be most reliable and predictable. The proposed system is TTC scheduler for Adaptive cruise control, is main control parts in driver assistant system. The controller is placed in host vehicle and minimum distance between host and front vehicle is maintained by using distance sensor for distance of front vehicle, speed sensor for getting speed of host vehicle and position sensor for getting the position of accelerator pedal. The controller vary the speed of the host vehicle so that to keep minimum distance. The system is simulated using keil IDE tool. The application tasks are scheduled by using efficient Time triggered cooperative scheduler. The scheduler is written in Embedded ‘c’ and Hardware consists of ARM 7 based microcontroller LPC2148.

Vehicle detection in aerial images is an important and challenging task in the field of remote sensing. Recently, deep learning technologies have yielded superior performance for object detection in remote sensing images. However, the detection results of the existing methods are horizontal bounding boxes that ignore vehicle orientations, thereby having limited applicability in scenes with dense vehicles or clutter backgrounds. In this article, we propose a one-stage, anchor-free detection approach to detect arbitrarily oriented vehicles in high-resolution aerial images. The vehicle detection task is transformed into a multitask learning problem by directly predicting high-level vehicle features via a fully convolutional network. That is, a classification subtask is created to look for vehicle central points and three regression subtasks are created to predict vehicle orientations, scales, and offsets of vehicle central points. First, coarse and fine feature maps outputted from different stages of a residual network are concatenated together by a feature pyramid fusion strategy. Upon the concatenated features, four convolutional layers are attached in parallel to predict high-level vehicle features. During training, task uncertainty learned from the training data is used to weight loss function in the multitask learning setting. For inferencing, oriented bounding boxes are generated using the predicted vehicle features, and oriented nonmaximum suppression (NMS) postprocessing is used to reduce redundant results. Experiments on two public aerial image data sets have shown the effectiveness of the proposed approach.

Maneuvering targets tracking in cluttered environment is a challenging problem in computer vision because of the difficulty of distinguishing the target from the background. In this paper, we treat tracking as a binary classification problem and employ support vector machine to suppress the background. In order to enhance the robustness against illumination changes, we propose to combine color invariance with traditional RGB values to train the SVM. First, we use expectation maximization algorithm to extract the target from the environment; then, RGB and color invariance values are used to train SVM. In the incoming frames, pixels in regions of interest are classified by SVM and the confidence map is produced, which will afterward be used by traditional tracking approach to track the target, in this paper, we employ particle filter. Experimental results on challenging sequences validate the effectiveness of the proposed method in cluttered background target tracking.