Young-Chul Lim

Stereo vision–based vehicle detection using a road feature and disparity histogram

This paper presents stereo vision-based vehicle detection approach on the road using a road feature and disparity histogram. It is not easy to detect only vehicles robustly on the road in various traffic situations, for example, a nonflat road or a multiple-obstacle situation. This paper focuses on the improvement of vehicle detection performance in various real traffic situations. The approach consists of three steps, namely obstacle localization, obstacle segmentation, and vehicle verification. First, we extract a road feature from v-disparity maps binarized using the most frequent values in each row and column, and adopt the extracted road feature as an obstacle criterion in column detection. However, many obstacles still coexist in each localized obstacle area. Thus, we divide the localized obstacle area into multiple obstacles using a disparity histogram and remerge the divided obstacles using four criteria parameters, namely the obstacle size, distance, and angle between the divided obstacles, and the difference of disparity values. Finally, we verify the vehicles using a depth map and gray image to improve the performance. We verify the performance of our proposed method by conducting experiments in various real traffic situations. The average recall rate of vehicle detection is 95.5%.

Distance estimation algorithm for both long and short ranges based on stereo vision system

We present a distance measurement method based on stereo vision system while guaranteeing accuracy and reliability. It has been considered as difficult problem to measure both long and short distance with a stereo vision system accurately due to sampling error and camera sensor error. To resolve these problems of the stereo vision system, we utilize an algorithm which is consisted of a modified sub-pixel displacement method to enhance the accuracy of disparity and strong tracking Kalman filter (STKF) to reduce the camera sensor errors. Our displacement method and the usefulness of STKF are verified as compared to other displacement methods and conventional Kalman filter (CKF) through simulating on the several distance ranges. The Monte-Carlo simulation results show that our algorithm is capable of measuring up to hundreds of meters while root mean square error (RMSE) maintains about 0.04 at all ranges, even though the target vehicle maneuvers or moves nonlinearly.

Obstacle localization with a binarized v-disparity map using local maximum frequency values in stereo vision

In this paper, we propose an obstacle localization method using column detection with a binarized v-disparity map. For localizing obstacles robustly in environments where there exist many obstacles, such as roadside trees, pedestrians, or where median strips exist, we also propose a new method which extracts a road feature. We create a binarized v-disparity map using local maximum frequency values in each row for emphasizing a diagonal straight line, namely a road feature. And to further eliminate noise, we use a comparing method which compares all road feature values with median values. Finally, we use a linear interpolation for rows which have no value. We can extract a road feature through this method robustly. And we adopt this new standard to localize obstacles. An experimental result which uses a real road image proved that our proposed method has the advantage of extracting a road feature and localizing obstacles in environments where many obstacles exist.

A fusion method of data association and virtual detection for minimizing track loss and false track

In this paper, we present a method to track multiple moving vehicles using the global nearest neighborhood (GNN) data association (DA) based on 2D global position and virtual detection based on motion tracking. Unlikely the single target tracking, multiple target tracking needs to associate observation-to-track pairs. DA is a process to determine which measurements are used to update each track. We use the GNN data association not to lost track and not to connect incorrect measurements. GNN is a simple, robust, and optimal technique for intelligent vehicle applications with a stereo vision system that can reliably estimates the position of a vehicle. However, an incomplete detection and recognition technique bring low track maintenance due to missed detections and false alarms. A complementary virtual detection method adds to GNN method. Virtual detection is used to recover the missed detection by motion tracking when the track maintains for some periods. Motion tracking estimates virtual region of interest (ROI) of the missed detection using a pyramidal Lukas-Kanade feature tracker. Next, GNN associates the lost tracks and virtual measurements if the measurement exists in the validation gate. Our experimental results show that our tracking method works well in a stereo vision system with incomplete detection and recognition ability.

A sliced synchronous iteration architecture for real-time global stereo matching

In this paper, we present a low memory-cost message iteration architecture for a fast belief propagation(BP) algorithm. To meet the real-time goal, our architecture basically follows multi-scale BP method and truncated linear smoothness cost model. We observe that the message iteration process in BP requires a huge intermediate buffer to store four directional messages of the whole node. Therefore, instead of updating all the node messages in each iteration sequence, we propose that individual node could be completed iteration process in ahead and consecutively execute it node by node. The key ideas in this paper focus on both maximizing architectures parallelism and minimizing implementation cost overhead. Therefore, we first apply a pipelined architecture to each iteration stage that is executed independently. Note that pipelining makes it faster message throughput at a single iteration cycle rather than consuming whole iteration cycle time as previously. We also make multiple message update nodes as a minimal processing unit to maximize the parallelism. For the multi-scale BP method, the proposed parallel architecture does not cause additional execution time for processing the nodes in the down-scaled Markov Random Field(MRF). Considering VGA image size, 4 iterations per each scale and 64 disparity levels, our approach can reduce memory complexity by 99.7% and make it 340 times faster than the general multi-scale BP architecture.

Stereo vision-based improving cascade classifier learning for vehicle detection

In this article, we describe an improved method of vehicle detection. AdaBoost, a classifier trained by adaptive boosting and originally developed for face detection, has become popular among computer vision researchers for vehicle detection. Although it is the choice of many researchers in the intelligent vehicle field, it tends to yield many false-positive results because of the poor discernment of its simple features. It is also excessively slow to processing speed as the classifiers detection window usually searches the entire input image. We propose a solution that overcomes both these disadvantages. The stereo vision technique allows us to produce a depth map, providing information on the distances of objects. With that information, we can define a region of interest (RoI) and restrict the vehicle search to that region only. This method simultaneously blocks false-positive results and reduces the computing time for detection. Our experiments prove the superiority of the proposed method.

Obstacle Categorization Based on Hybridizing Global and Local Features

We propose a novel obstacle categorization model combining global feature with local feature to identify cars, pedestrians and unknown backgrounds. A new obstacle identification method, which is hybrid the global feature and local feature, is proposed for robustly recognizing an obstacle with and without occlusion. For the global analysis, we propose the modified GIST based on biologically motivated the C1 feature, which is robust to image translation. We also propose the local feature based categorization model for recognizing partially occluded obstacle. The local feature is composed of orientation information at a salient position based on the C1 feature. A classifier based on the Support Vector Machine (SVM) is designed to classify these two features as cars, pedestrians and unknown backgrounds. Finally, all classified results are combined. Mainly, the obstacle categorization model makes a decision based on the global feature analysis. Since the global feature cannot express partially occluded obstacle, the local feature based model verifies the result of the global feature based model when the result is an unknown background. Experimental results show that the proposed model successfully categorizes obstacles including partially occluded obstacles.

MCMC particle filter-based vehicle tracking method using multiple hypotheses and appearance model

In this study, we propose a multiple vehicle tracking method using multiple hypotheses and the appearance model. The multiple hypotheses are associated with multiple tracks using track-to-multiple hypotheses association method. A target state is estimated using the maximum a posteriori probability estimation method. The posterior probability is proportional to the product of a priori probability and the likelihood that is calculated using similarities of multiple hypotheses and the appearance model. The posterior probability density function is estimated using the Markov chain Monte Carlo particle filter. An optimal posterior target state is determined using a sample with the maximum a posteriori probability. Our experimental results show that the proposed method can improve multiple objects tracking precision as well as multiple object tracking accuracy.

Dynamic obstacle identification based on global and local features for a driver assistance system

This paper proposes a novel dynamic obstacle recognition system combining global feature with local feature to identify vehicles, pedestrians and unknown backgrounds for a driver assistance system. The proposed system consists of two main procedures: a dynamic obstacle detection model to localize an area containing a moving obstacle, and an obstacle identification model, which is a hybrid of global and local information, for recognizing an obstacle with and without occlusion. A dynamic saliency map is used for localizing an area containing a moving obstacle. For the global feature analysis, we propose a modified GIST using orientation features with MAX pooling, which is robust to translation and size variations of an object. Although the global features are a compact way to represent an object and provide a good accuracy for non-occluded objects, they are sensitive to image translation and occlusion. Thus, a local feature-based identification model is also proposed and combined with the global feature. As such, for the obstacle identification problem, the proposed system mainly follows the global feature-based object identification. If the global feature-based model identifies a candidate area as background, the system verifies the area again using the local feature-based model. As a result, the proposed system is able to provide information on both the appearance of obstacles and the class of an obstacle. Experimental results show that the proposed model can successfully detect obstacle candidates and robustly identify obstacles with and without occlusion.

Stereo vision-based obstacle detection using dense disparity map

In this paper, we propose stereo vision-based obstacle detection method on the road using a dense disparity map. We use the dense disparity map to detect obstacles robustly in real traffic situations. Our method consists of three stages, namely road feature extraction, column detection, obstacle segmentation. First, we extract a road feature from a v- disparity map calculated from a dense disparity map. And we perform a column detection using the extracted road feature as a criterion that decides whether obstacles exist or not. Finally, we perform a segmentation using a birds-eye view mapping to divide the merged obstacle into each obstacle accurately. We conduct experiments to verify our method in the real traffic situations.