• ETRI Journal
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• v.36 no.3
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• pp.439-449
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• 2014

In this paper, a novel vision-based nighttime vehicle detection approach is presented, combining state machines and downhill simplex optimization. In the proposed approach, vehicle detection is modeled as a sequential state transition problem; that is, vehicle arrival, moving, and departure at a chosen detection area. More specifically, the number of bright pixels and their differences, in a chosen area of interest, are calculated and fed into the proposed state machine to detect vehicles. After a vehicle is detected, the location of the headlights is determined using the downhill simplex method. In the proposed optimization process, various headlights were evaluated for possible headlight positions on the detected vehicles; allowing for an optimal headlight position to be located. Simulation results were provided to show the robustness of the proposed approach for nighttime vehicle and headlight detection.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.61-70
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• 2017

In This Paper, the Classification Speed of Vehicle Headlight Modules is Improved by a CPU-based Parallel Processing Using OpenMP. Also, a Classification Method of Headlight Modules which Extracts their Features after Revising their Alignment is Proposed. To Analyze the Performance of the Proposed Method, the Discrimination Accuracy and the Processing Speed were Compared with the Method Using Gray Image and the Method Using Line Detection. As the Results of the Analysis, in the Discrimination Accuracy, the Proposed Method and the Line Detection Method Showed good Performance, but the Proposed Method Showed Better Performance than the Line Detection Method by the Processing Speed. Also, the Gray-based Method was the Best in Processing Speed, but the Proposed Method is Better than the Gray-based Method in the Discrimination Accuracy.

• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.138-148
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• 2015

Object detection methods based on background learning are widely used in video surveillance. However, when a car runs with headlights on, these methods are likely to detect the car region and the area illuminated by the headlights as one connected change region. This paper describes a method of separating the car region from the area illuminated by the headlights. First, we detect change regions with a background learning method, and extract blobs, connected components in the detected change region. If a blob is larger than the maximum object size, we extract candidate object regions from the blob by clustering the intensity histogram of the frame difference between the mean of background images and an input image. Finally, we compute the similarity between the mean of background images and the input image within each candidate region and select a candidate region with weak similarity as an object region.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.3
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• pp.181-189
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• 2004

This paper describes the method for detecting vehicles in the rear and rear-side at night by using headlight features. A headlight is the outstanding feature that can be used to discriminate a vehicle from a dark background. In the segmentation process, a night image is transformed to a binary image that consists of black background and white regions by gray-level thresholding, and noise in the binary image is eliminated by a morphological operation. In the feature extraction process, the geometric features and moment invariant features of a headlight are defined, and they are measured in each segmented region. Regions that are not appropriate to a headlight are filtered by using geometric feature measurement. In region classification, a pair of headlights is detected by using relational features based on the symmetry of a pair of headlights. Experimental results show that this method is very applicable to an approaching vehicle detection system at nighttime.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2962-2964
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• 1999

We know the traffic information about the velocity and position of vehicle by extraction and tracking vehicle from continuosly obtained road image of camera. The conventional method of vehicle detection indicate increment of error due to headlight and taillight in night road image. This paper show such as vehicle detection of binary, Edge detection. amalgamation of image are applied to extract the vehicle, and Kalman filter is adaptive methods for tracking position and velocity of vehicle.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.297-305
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• 2015

AFLS (Adaptive front lighting System) is being applied to improve safety in driving automotive at night. Safe embedded system for controlling head-lamp has to be tightly designed by considering safety requirement of hardware-dependent software, which is embedded in automotive ECU(Electronic Control Unit) hardware under severe environmental noise. In this paper, we propose an adaptive headlight controller with newly-designed symmetric angle sensor compensator, which is integrated with ECU-based adaptive front light system. The proposed system, on which additional backup hardware and emergency control algorithm are integrated, effectively detects abnormal situation and restore safe status of controlling the light-angle in AFLS operations by comparing result in symmetric angle sensor. The controlled angle value is traced into internal memory in runtime and will be continuously compared with the pre-defined lookup table (LUT) with symmetric angle value, which is used in normal operation. The watch-dog concept, which is based on using angle sensor and control-value tracer, enables quick response to restore safe light-controlling state by performing the backup sequence in emergency situation.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.5
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• pp.311-317
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• 2011

The blind spot is an area where drivers visibility does not reach. When drivers change a lane to adjacent lane, they need to give an attention because of the blind spot. If drivers try to change lane without notice of vehicle approaching in the blind spot, it causes a reason to have a car accident. This paper proposes a night-time blind spot vehicle detection using cameras. At nighttime, head-lights are used as characteristics to detect vehicles. Candidates of headlight are selected by high luminance feature and then shape filter and kalman filter are employed to remove other noisy blobs having similar luminance to head-lights. In addition, vehicle position is estimated from detected head-light, using virtual center line represented by approximated the first order linear equation. Experiments show that proposed method has relatively high detection porformance in clear weather independent to the road types, but has not sufficient performance in rainy weather because of various ground reflectors.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.1
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• pp.112-121
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• 2016

AFLS (Adaptive front lighting system) is being applied to improve safety in driving automotive at night. Safe embedded system design for controlling head-lamps is required to improve noise robust ECU hardware and software simultaneously by considering safety requirement of hardware-dependent software under severe environmental noise. In this paper, we propose an adaptive headlight controller with a newly-designed symmetric angle sensor compensator, especially based on the proposed steering-swivel angle lookup table to determine whether the current controlling target is safe. The proposed system includes an additional backup hardware to compare the system status and provides safe swivel-angle management using a controlling algorithm based on the pre-defined lookup table (LUT), which is a symmetric mapping relationship between the requested steering angle and expected swivel angle target. The implemented system model shows that the proposed architecture effectively detects abnormal situations and restores safe status of controlling the light-angle in AFLS operations under severe noisy environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.9
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• pp.780-785
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• 2012

In the current study, the authors propose a method for extracting license plate regions by means of a neural network trained to output the plates center of gravity. The method is shown to be effective. Since the learning pattern presentation positions are defined by random numbers, a different pattern is submitted to the neural network for learning each time, which enables it to form a neural network with high universality of coverage. The article discusses issues of the optimal learning surface for a license plate covered by the learning pattern, the effect of suppression learning of the number and headlight sections, as well as the effect of learning pattern enlargement/reduction and of concentration value conversion. Results of evaluation tests based on pictures of 595 vehicles taken at an underground parking garage demonstrated detection rates of 98.5%.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.17-24
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• 2009

This paper suggests the background image and the algorism of detecting an accident at crossroads by using the sequence of traffic light at crossroads, which is installed within the crossroads, in order to detect an accident within crossroads. A method of using the existing image contains a problem that the accident-detection ratio gets lower in a situation that noise occurs loudly given using new accident model, the confused situation, or sound source. This study used the accident detection by developing a filter of using the property of histogram in the sequence of traffic light at crossroads and the background image, in order to reduce misjudgment of an accident caused by external shadow, vehicle stoppage, vehicle headlight, and externally environmental influence. As a result of experimenting by acquiring 15 actual accident images in order to examine the performance of the suggested algorism, the accident was detected in all the 15 videos. Even as for a new accident model, the accident within crossroads could be detected.