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This study describes a method for using a camera to automatically recognize the speed limits on speed-limit signs. This method consists of the following three processes: first (1) a method of detecting the speed-limit signs with a machine learning method utilizing the local binary pattern (LBP) feature quantities as information helpful for identification, then (2) an image processing method using...
In this paper, we propose an intelligent auto-dipping system that will be placed on the dashboard of the vehicle. The moment it detects headlights of an oncoming vehicle, the high beam of host vehicle will be dipped automatically, with a flash of dipper to signal the oncoming vehicle. The system will be most effective when every vehicle has this auto-dipper installed. This system would help prevent...
In most of the traffic safety studies, both the identification of high-risk locations and the assessment of safety improvement solutions are done through the use of historical crash data. This study proposes an alternative approach that makes use of traffic conflicts extracted from traffic video recordings for safety assessment. State-of-the-art computer vision techniques are used to extract vehicle...
Over the last decade, there have been many studies that focus on modeling driver behavior, and in particular detecting and overcoming driver distraction in an effort to reduce accidents caused by driver negligence. Such studies assume that the entire onus of avoiding accidents are on the driver alone. In this study, we adopt a different stance and study the behavior of pedestrians instead. In particular,...
In recent years, there has been an interest in detailed monitoring of road traffic, particularly in intersections, in order to obtain a statistical model of the flow of vehicles through them. These models aid in the optimization of traffic management and allow for smarter transportation systems. While conventional methods sensors at each of the intersections entrances/exits allow for counting, are...
Estimating the number of vehicles present in traffic video sequences is a common task in applications such as active traffic management and automated route planning. There exist several vehicle counting methods such as Particle Filtering or Headlight Detection, among others. Although Principal Component Pursuit (PCP) is considered to be the state-of-the-art for video background modeling, it has not...
The percentage of false alarms caused by spiders in automated surveillance can range from 20–50%. False alarms increase the workload of surveillance personnel validating the alarms and the maintenance labor cost associated with regular cleaning of webs. We propose a novel, cost effective method to detect false alarms triggered by spiders/webs in surveillance camera networks. This is accomplished by...
We extract 3D curb from video sequence, using a single camera equipped with fish-eye lens and located at the front/rear of the vehicle. The challenge in extracting curbs from images lies in their small size and their lack of texture. We show that by appropriately exploiting appearance features, 3D geometry, and temporal information, one can reliably detect and localize the curbs in the 3D scene. The...
To solve the problem of low efficiency caused by the heavy traffic in the gas station at the peak time, a method for real-time vehicle detection and tracking using Adaboosting classifier and optical flow tracking is proposed in this paper. The Adaboosting algorithm is used to train the classifier with Haar-like feature extracted from positive samples and negative samples of the gas station vehicles...
This paper presents a visual attention based convolutional neural network (CNN) to solve the image classification problem in the real complex world scene. The presented method can simulate the process of recognizing objects and find the area of interest which is related with the task. Compared with the CNN method in image classification, the model is proficient in fine-grained classification problem...
Pedestrian detection is an important key problem in Advanced Driver Assistance Systems (ADAS). Un-signalized pedestrian crossing zone are dangerous places, where pedestrians enter the lane suddenly. This is the main factor for most of the accidents. For that, this paper illustrates a machine learning approach for detecting the pedestrian zone and also to detect the pedestrians crossing in that zone...
This paper proposes a new approach of abnormal vehicle detection for frontal and lateral collision warnings in nighttime driving using monocular vision. Motion information is used to estimate moving objects. An empirical threshold range is introduced to eliminate efficiently most of non-vehicle regions. Vehicle candidates are segmented by using K-means clustering. An analysis is performed carefully...
Traffic density estimation plays an integral role in intelligent transportation systems (ITS), using which provides important information for signal control and effective traffic management. In this paper, we present a new framework for traffic density estimation based on topic model, which is an unsupervised model. This framework uses a set of visual features without any need to individual vehicle...
This paper proposes a more accurate way to detect the logistics driver fatigue state. In logistics transportation vehicle driving process, there are differences between the blinking frequency and the closing time of logistics driver's eyes. According to the difference, the logistics driver's fatigue state can be able to be detected. Using the computer, it can strengthen the processing to the image...
Pedestrian motion type classification is proposed in this work. The model incorporates the pedestrian pose recognition and lateral speed, motion direction and spatial layout of the environment. Pedestrian poses are recognized according to the spatial body language ratio. The center of mass of the body relative to its width and height is used to define the pedestrian pose. Motion trajectory is obtained...
We present an automated mechanism that can detect and issue warnings of machinery threat such as the presence of construction vehicles on pipeline right-of-way. The proposed scheme models the human visual perception concepts to extract fine details of objects by utilizing the corners and gradient histogram information in pyramid levels. Two real-world aerial image datasets are used for testing and...
This paper presents a popular method called boosted hog features to detect pedestrians and vehicles in static images. We compared the differences and similarities of detecting pedestrians and vehicles, then we selected boosted hog features to get an satisfying result. In the part of detecting pedestrians, Histograms of Oriented Gradients (HOG) feature is applied as the basic feature due to its good...
In traffic video surveillance system, target-level tracking and feature-level tracking are two important areas for research. Therefore, the combination between them is an interesting question. Mean-shift is a traditional target-level tracking algorithm with no adaptation to vehicle scale and orientation change. In order to solve the problem, algorithm combine SURF (speed-up robust feature) feature...
Road In this paper, we focus on both the road vehicle and pedestrians detection, namely obstacle detection. At the same time, a new obstacle detection and classification technique in dynamical background is proposed. Obstacle detection is based on inverse perspective mapping and homography. Obstacle classification is based on fuzzy neural network. The estimation of the vanishing point relies on feature...
In this research paper Objects are detected and recognized in cluttered scene. We use Harris Corner Detector to extract interest points, and use additional descriptor FREAK (Fast Retina Keypoint) to match and find detect the object. We also use some classification algorithm to classify and label the object based on the extracted features. The proposed techniques are precise and robust.
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