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Up until now, law violation and drowsiness have been major causes of road traffic accident in Thailand. This study presents an approach to detect the risk due to drowsiness and distraction of a driver. In our method, techniques in computer vision are employed to extract facial features of the driver to determine his behaviors. Since the vehicle speed is also an important factor of the accident risk,...
The traffic sign detection and recognition is an integral part of Advanced Driver Assistance System (ADAS). Traffic signs provide information about the traffic rules, road conditions and route directions and assist the drivers for better and safe driving. Traffic sign detection and recognition system has two main stages: The first stage involves the traffic sign localization and the second stage classifies...
In this paper, we present a learning-based brake light classification algorithm for intelligent driver-assistance systems. State-of-the-art approaches apply different image processing techniques with hand-crafted features to determine whether brake lights are on or off. In contrast, we learn a brake light classifier based on discriminative color descriptors and convolutional features fine-tuned for...
Extracting hand regions and their grasp information from images robustly in real-time is critical for occupants' safety and in-vehicular infotainment applications. It must however, be noted that naturalistic driving scenes suffer from rapidly changing illumination and occlusion. This is aggravated by the fact that hands are highly deformable objects, and change in appearance frequently. This work...
In this paper, we propose a vision-based traffic light and arrow detection algorithm for intelligent vehicles. We detect all three traffic light colours along with the arrow direction robustly for varying illuminations and traffic lights. A fine-tuned convolutional neural network is used in an offline phase to localise the traffic light region-of-interest within a given camera image. Given the constrained...
Being robust to changeable illumination and shadow are essential requirements of a practical vehicle counting system. In this paper, we come up with a method that detects vehicle by analyzing the state of laser line projected on lane. In addition, we use the entropy of histogram of oriented gradients (HOG) descriptor extracted from the region of interest (ROI) to quantify area percent of vehicle in...
We introduce a new computer vision based system for robust traffic sign recognition and tracking. Such a system presents a vital support for driver assistance in an intelligent automotive. Firstly, a color based segmentation method is applied to generate traffic sign candidate regions. Secondly, the HoG features are extracted to encode the detected traffic signs and then generating the feature vector...
In this paper, a motion-based vehicle detection is proposed to detect the vehicles in Hsuehshan Tunnel. Camera vision based vehicle detection in the long tunnel is a challenging problem. The video quality emerging from camera is affected by dynamic illumination environment, time variant, camera resolution, camera aging, camera position, camera view angle, heterogeneous camera, and vehicle speed. Besides,...
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...
Detecting an illegally parked vehicle in urban scenes of traffic monitoring system becomes more complex task due to occlusions, lighting changes, and other factors. In this paper, a new framework to detect illegally parked vehicle using dual background model subtraction is presented. In our system, the adaptive background model is generated based on statistical information of pixel intensity that...
Extracting driver's facial feature helps to identify the vigilance level of a driver. Some research about facial feature extraction also has been developed for controlled interface of vehicle. To acquire facial feature of drivers, research using various visual sensors have been reported. However, potential challenges to such a work include rapid illumination variation resulting from ambient lights,...
The proposed system comes in the context of intelligent parking lots management and presents an approach for vacant parking spots detection and localization. Our system provides a camera-based solution, which can deal with outdoor parking lots. It returns the real time states of the parking lots providing the number of available vacant places and its specific positions in order to guide the drivers...
In this paper, we proposed a driver drowsiness detection method for which only eyelid movement information was required. The proposed method consists of two major parts. 1) In order to obtain accurate eye openness estimation, a vision based eye openness recognition method was proposed to obtain an regression model that directly gave degree of eye openness from a low-resolution eye image without complex...
This paper presents a quantum particle swarm optimization solutions for the problem of license plate location, the first image of the vehicle local area adaptive illumination compensation image preprocessing, improving the quality of the picture. Then use the YCbCr color model signals a band compression to achieve illumination compensation. Based on this paper proposes a quantum particle swarm optimization...
Our aim is to automatically detect the road borders in a road scene image. This is useful to many road scenes analysis applications, in both fields of vehicle guidance and civil engineering. Difficulties arise because pavements are often heterogeneous and because illumination variations often occur in outdoor scenes. Some vehicle navigation projects use colour images for road borders detection [1,...
We present a TLD-based vehicle tracking method, which uses HOG that is precomputed in the detection process, and an online SVM re-detector. We perform HOG-based tracking in EHMI. When the tracking fails, the system performs redetection for neighboring regions. Tracked vehicles are reused as positive data for re-learning. Therefore, the proposed system performs robust tracking without additional computation.
In this paper, we investigated the deep learning model for object classification. Robust classification networks were trained based on Deep Belief Networks (DBN) combined with several object representations included image pixel value, feature histogram by Histogram of Oriented Gradients (HOG) operator and eigen-features to distinguish four categories: pedestrian, biker, vehicle and others in the real...
The classical mean shift algorithm is easy to pass into local maxima, which is caused by the lack of appropriate target model updating mechanism. In this paper, a SIFT-based mean shift algorithm is proposed, which can be used for continuous vehicle tracking in complex situations, such as the shape and the illumination of the vehicle object change. In our algorithm, the mean shift algorithm is utilized...
Understanding the intention of other road users is a key requirement for autonomous driving. In this regard, one particularly relevant cue is a flashing turn signal, since it gives an important hint regarding the intended driving direction of another vehicle in the next few seconds. As such, turn signals can be considered as one of the first methods invented for car-to-car communication. In contrast...
The ability to classify a vehicle is of extreme importance for both civilian and non-civilian applications. For non-civilian applications the state-of-the-art leaves much to be desired, as hierarchal and real-time classification have yet to be truly investigated. This paper provides a survey of the current state-of-the-art in vehicle classification and provides recommendations for future research...
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