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To achieve the goal of frontal vehicle detection in night-driving condition, we propose an effective method to detect the red taillights of vehicles. The challenge is that the taillight images captured with automatic exposure typically are overexposed, which makes red color segmentation often erroneous. Instead of customizing the camera hardware to tackle this problem, we combine morphological and...
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...
Vehicle detection and recognition from aerial imagery provides useful information for local vehicle volume estimation and traffic monitoring. In this paper, we propose a method that accurately detects vehicles in urban environment using a probabilistic classification method followed by a refinement based on object segments. Both classification and segmentation methods make use of coregistered aerial...
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 study, we developed a driver-assistance system on Lane Detection during nighttime by mounting a CCD camera inside the car to capture images and to use computer visions to detect lanes and the driving condition in front of the vehicle. This system can increase the safety of driving during low light condition. The features of this system includes: lane detection, surrounding vehicles detection,...
In this paper an automated vehicle detection and traffic density estimation algorithm has been developed and validated for very high resolution satellite video data. The algorithm is based on an adaptive background estimation procedure followed by a background subtraction at every video frame. The vehicle detection is performed through a further mathematical morphology and statistical analysis on...
This paper proposes a method to track vehicle in highway using CAMShift-based method. The Continuously Adaptive Mean Shift (CAMShift) is a well-known algorithm in object tracking. However, the ordinary CAMShift works fairly well only for tracking object that can identify by hue, when the difference between object color and background is large. This is not the case in vehicle tracking. The objective...
This paper proposes a new vehicle color classification scheme to identify vehicles with their colors. To detect vehicles from roads, the paper proposes a novel symmetrical descriptor to determine the ROI of each vehicle without using any motion features. This scheme provides two advantages; there is no need of background subtraction and it is extremely efficient for real-time applications. After detection,...
Detecting moving objects in videos is an important task in many computer vision applications, including human interaction, traffic monitoring and Structural Health Monitoring. When having a stationary camera, a basic method to detect the objects of interest is background subtraction. However, precise moving object detection using such a method is an extremely difficult task in a varying environment...
A stereo vision based road scene segment and vehicle detection method was proposed in this paper. In the method, First, dynamic programming was used for stereo matching, and then mismatching pixels were removed by left-right-consistency check; Second, V disparity was built by computing the disparity map, and a fast projection based line detection method was used to detect lines in V disparity map,...
The suspect vehicle detection system normally compares the list of criminal license plates and vehicle license plates gathering from various sensors in order to identify the criminal vehicles or the suspect vehicles. However, the traditional process of comparing those license plates utilizing the matching of alphabet character is not effective. If the characters do not match any one character, the...
Vehicle detection and tracking plays an effective and significant role in the area of traffic surveillance system where efficient traffic management and safety is the main concern. In this paper, we discuss and address the issue of detecting vehicle / traffic data from video frames. Although various researches have been done in this area and many methods have been implemented, still this area has...
In this work, to recognize the direction of turn signals, reflectance is decomposed from the original image. Instead of using heuristic features such as the symmetry, position and size of the rear-view vehicle, we focus on finding the invariant features to model turn signal scattering by scatter properties and then turn signals detection can be performed in a part-based manner. Experiment results...
Vehicle counting system has a wide range of applications, from visual surveillance to intelligent transportation. Due to the different lighting conditions during the day and night, there is not a unified method to capture vehicles. To address this problem, we present unified vehicle detection and counting algorithm based on a new multiple feature background models using morphology and color difference...
In complex urban traffic conditions, the accurate detection of vehicles is challenging to current vehicle detection methods. To achieve the precise vehicle detection in complex urban traffic conditions, we have proposed a vehicle detection method based on a deformable hybrid image template in this paper. Our method contains two steps: constructing our hybrid image template and its probability model,...
In complex urban traffic conditions, occlusion between vehicles is a common problem which is challenging to current vehicle detection methods. In this paper, we have proposed a vehicle detection method based on a part-based model which can deal with the occlusion problem. Our method includes two steps: constructing the part-based model and detecting vehicles from traffic images. In the first step,...
With rapid development of intelligent transportation systems (ITS), video-based road monitoring systems become increasingly popular. Most video processing systems concerns the detection and monitoring of all road vehicles. Few research refers to non-social vehicle detection specially, although it is critical for urban traffic management. In this paper, we present a video-based taxi detection system...
This paper proposes a novel parking spaces detection algorithm which is based on image segmentation and local binary pattern. The vehicles are usually contains a lot of compositions, while the vacant parking spaces' composition is relatively small. According to this characteristic, we segment the parking image. To judge whether each parking area has a large number of small split or not, can achieve...
This paper proposes a vision based multiple vehicle detection and tracking system. Vehicle tail light information is used to localize vehicle potential region, then each candidate is verified by a back propagation neural network (BPNN) trained by Gabor feature set. In the multiple vehicle tracking stage, multiple scale vehicle tracking, same color vehicle occlusion and observation model updating problem...
In this paper, a clustering method of adjacent frames is proposed for vehicle flow statistics to overcome the fault of low robustness of video-based detection algorithms in complex environments. In the method, the boundaries of the abrupt or gradual visual content changing in consecutive video frames are described by color and intensity histogram method. The clustered frames containing different vehicles...
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