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Automated driving functions are under intensified development by industry and academia since the last decade. Due to the large operation space and various complex scenarios automated driving functions have to cope with, assessment efforts are expected to rise dramatically. In order to quantify benefits and risks of these functions in an efficient way, this paper describes a holistic approach for the...
Traffic monitoring systems play a key role in the transition toward smart cities and more efficient transportation systems. Roads and highways are now equipped with sensors to measure traffic flow, predict congestion, and adaptively control traffic routes. Traffic planning applications rely on sensors to provide accurate information about vehicles and infrastructure status. Vehicle class is an important...
In this paper, we consider a single sensor classification problem, focusing on classifying the types of the moving vehicles. To improve the classification accuracy with low-time complexity in complex scenes, the acoustic sensor data sets were captured to measure the physical events and a novel hybrid dictionary learning method for vehicle classification is proposed. The efficient hybrid dictionary...
Time series data are ubiquitous and are of importance in many application problems in engineering, science, medicine, economics and entertainment. Many real world pattern classification problems involve the processing and analysis of multiple variables in the temporal domain. These types of problems are referred to as Multivariate Time Series (MTS) problems. In many real-world applications, an MTS...
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...
Target tracking is an important requirement in the battle field. The proposed target classification algorithm is a video based surveillance system. This kind of system is very useful in tracking and classification of targets. The target area and tyre detection are the key steps of this algorithm used for detection of target. A target classification algorithm based on area and tire axle spacing was...
Fatality due to road accidents are increasing with the increase in population and number of vehicles. Intelligent systems are developed to counter act the loss due to road accidents. The paper proposes one such method to counter the accidents by the implementation of pedestrian detection by the use of LBP histogram and HAAR-like features. LBP histogram are used for cross checking the HAAR-like features...
Automation techniques have been applied in almost every field in past few years. Automated Guided Vehicle (AGV) are most often used in industries and inventories for object management. Obstacle avoidance being a necessary requirement for navigation in any vehicle, still faces many challenges in the field of automation due to uncertain nature of the surrounding environment. This paper presents the...
To enhance the robustness of the vehicle detection system, an effective algorithm to identify the lighting conditions (daylight, night, lowlight (dawn, dusk)) based on histogram analysis is presented in this paper. The algorithm consists of two procedures: extracting and updating background image, and generating a lighting conditions classifier based on background image analysis. The algorithm is...
In this paper, we propose an in-node microprocessor-based vehicle classification approach to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. Our approach for vehicle classification utilizes J48 classification algorithm implemented in Weka (a machine learning software suite). J48 is a Quinlan's C4.5 algorithm, an extension of decision tree machine learning based...
In this paper, a real-time system for pedestrian detection in traffic scenes is proposed. It takes the advantage of having a pair of stereo video-cameras for acquiring the image frames and uses a sub-pixel level optimized semi-global matching (SORT-SGM) based stereo reconstruction for computing the dense 3D points map with high accuracy. A multiple paradigm detection module considering 2D, 3D and...
This paper presents an approach method to detect vehicle color and to classify multi vehicle from video data. The multi vehicle is classified by blob analysis and feature-based. The proposed method uses a video file recorded by traffic surveillance camera as input. This technique applied RGB (Red, Green and Blue) to detect color of vehicle image. The vehicle is separated from background by using optical...
This paper proposes a method of acoustic vehicle classification. We extract acoustic feature of wheeled vehicle and track vehicle based on the optimum wavelet packet energy and classify these two types of vehicle based on the fuzzy classifier. For evaluation purposes, real data are used from DARPA's SensIT project. The experiment results show that the proposed method can improve the correct recognition...
Future advanced driver assistant systems and automated driving put high demands on the environmental perception especially in urban environments. Major tasks are the perception of static and dynamic elements along with the drivable area and road structures. Although these tasks can be done without an explicit representation of the ground surface, evaluations with real-world sensor data have shown...
Most of the fatal injuries and the loss of lives occur due to lack of timely and quick action to be taken by the vehicular drivers. The difficulty in determining the incidence of fatigue-related accidents is due to the difficulty in identifying fatigue as a causal or causative factor in accidents. In most instances, one or more indirect or circumstantial pieces of evidence are used to make the case...
This paper proposes a novel vehicle color classification method which uses the concept of probabilistic latent semantic analysis (pLSA) to overcome the problem of sparse representation in data classification. Sparse representation is widely used and quite successful in many vision-based applications. However, it needs to calculate the sparse reconstruction cost (SRC) of each sample to find the best...
In this paper, a robust traffic sign recognition system is introduced for driver assistance applications and/or autonomous cars. The system incorporates two major operations, traffic sign detection and classification. The sign detection is based on color segmentation and incorporates hue detection, morphological filter and labeling. A nearest neighbor classifier is introduced for sign classification...
The fatigue level of the driver changes due to many factors such as monotonous job of continuous attentive driving, road traffic, un-healthy road conditions, insufficient sleep, stress level, changing work habits and adverse environmental conditions. This paper provides an alternative approach for design of fatigue classifier for vehicular drivers using Skin Conductance (SC) signal to save the loss...
Vehicle classification has crop up as an important field of study due of its importance in variety of applications like surveillance, security framework, traffic congestion prevention and accidents avoidance etc. The image sequences for traffic scenes are recorded by a stationary NI smart camera. The video clip is processed in LabVIEW to detect vehicle and measure characteristics like width, length,...
This paper presents a novel real-time scale adaptive visual tracking framework and its use in smart traffic monitoring where the framework robustly detects and tracks vehicles from a stationary camera. Existing visual tracking methods often employ semi-supervised appearance models where a set of samples are continuously extracted around the object to train a discriminant classifier between the vehicle...
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