Hsu-Yung Cheng

Lane Detection With Moving Vehicles in the Traffic Scenes

A lane-detection method aimed at handling moving vehicles in the traffic scenes is proposed in this brief. First, lane marks are extracted based on color information. The extraction of lane-mark colors is designed in a way that is not affected by illumination changes and the proportion of space that vehicles on the road occupy. Next, for vehicles that have the same colors as the lane marks, we utilize size, shape, and motion information to distinguish them from the real lane marks. The mechanism effectively eliminates the influence of passing vehicles when performing lane detection. Finally, pixels in the extracted lane-mark mask are accumulated to find the boundary lines of the lane. The proposed algorithm is able to robustly find the left and right boundary lines of the lane and is not affected by the passing traffic. Experimental results show that the proposed method works well on marked roads in various lighting conditions

Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks

We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixelwise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixelwise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and nonvehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of the Canny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixelwise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles.

Motion detection via change-point detection for cumulative histograms of ratio images

Motion detection is widely used as the key module for moving object extraction from image frames. In most of the motion detection methods, backgrounds are subtracted from captured images. This is called background subtraction. As standard intensity can be expressed as the multiplication of illumination and reflectance, illumination changes will produce a poor difference image from background subtraction and affect the accuracy of motion detection. In this paper, we use ratio images as the basis for motion detection. For thresholding the target images, we propose change-point detection for cumulative histograms to prevent the difficulties of searching peaks and valleys in histograms. Experimental results show that change-point detection of cumulative histograms performs very well for thresholding the target images. In addition, the superiority of motion detection based on ratio images to motion detection based on difference images is also depicted in experimentation.

Intelligent Highway Traffic Surveillance With Self-Diagnosis Abilities

In this paper, we propose a self-diagnosing intelligent highway surveillance system and design effective solutions for both daytime and nighttime traffic surveillance. For daytime surveillance, vehicles are detected via background modeling. For nighttime videos, headlights of vehicles need to be located and paired for vehicle detection. An algorithm based on likelihood computation is developed to pair the headlights of vehicles at night. Moreover, to balance between the robustness and abundance of acquired information, the proposed system adapts different strategies under different traffic conditions. Performing tracking would be preferred when traffic is smooth. However, under congestion conditions, it is better to obtain traffic parameters by estimation. We utilize a time-varying adaptive system state transition matrix in Kalman filter for better prediction in a traffic surveillance scene when performing tracking. We also propose a mechanism for estimating the traffic flow parameter via regression analysis. The experimental results have shown that the self-diagnosis ability and the modules designed for the system make the proposed system robust and reliable.

Integrated video object tracking with applications in trajectory-based event detection

This work presents an automated and integrated framework that robustly tracks multiple targets for video-based event detection applications. Integrating the advantages of adaptive particle sampling and mathematical tractability of Kalman filtering, the proposed tracking system achieves both high tracking accuracy and computational simplicity. Occlusion and segmentation error cases are analyzed and resolved by constructing measurement candidates via adaptive particle sampling and an enhanced version of probabilistic data association. Also, we integrate the initial occlusion handling module in the tracking system to backtrack and correct the object trajectories. The reliable tracking results can serve as the foundation for automatic event detection. We also demonstrate event detection by classifying the trajectories of the tracked objects from both traffic monitoring and human surveillance applications. The experimental results have shown that the proposed tracking mechanism can solve the occlusion and segmentation error problems effectively and the events can be detected with high accuracy.

Tracking of multiple objects across multiple cameras with overlapping and non-overlapping views

In this paper, we propose a fully automated approach for tracking of multiple objects across multiple cameras with overlapping and non-overlapping views in a unified framework without initial training. For single camera cases, Kalman filter and adaptive particle sampling are integrated for multiple objects tracking. When extended to multiple cameras cases, the relations between adjacent cameras are learned systematically by using image registration techniques for consistent handoff of tracking-object labels across cameras. In addition, object appearance measurement is employed to validate the labeling results. Experimental results demonstrate the performance of our approach on real video sequences for cameras with overlapping and non-overlapping views.

Multiple-Target Tracking for Crossroad Traffic Utilizing Modified Probabilistic Data Association

A multiple-target tracking system aimed at analyzing crossroad traffic systematically is proposed in this paper. The proposed mechanism is based on Kalman filtering and modified probabilistic data association. Unlike traditional Kalman filtering tracking, the proposed mechanism constructs candidate measurement lists by matching the sizes of the measurements and the targets first. When the sizes do not match, object matching within a limited area is performed. Also, we modify the classical probabilistic data association method to enhance its performance and make it more suitable for vision-based systems. The proposed mechanism, which can serve as the foundation for automatic traffic event detection, can solve the occlusion problems effectively without incurring too much computational complexity.

Efficient human action and gait analysis using multiresolution motion energy histogram

Average Motion Energy (AME) image is a good way to describe human motions. However, it has to face the computation efficiency problem with the increasing number of database templates. In this paper, we propose a histogram-based approach to improve the computation efficiency. We convert the human action/gait recognition problem to a histogram matching problem. In order to speed up the recognition process, we adopt a multiresolution structure on the Motion Energy Histogram (MEH). To utilize the multiresolution structure more efficiently, we propose an automated uneven partitioning method which is achieved by utilizing the quadtree decomposition results of MEH. In that case, the computation time is only relevant to the number of partitioned histogram bins, which is much less than the AME method. Two applications, action recognition and gait classification, are conducted in the experiments to demonstrate the feasibility and validity of the proposed approach.

Implementing automatic location update for follow-me database using VoIP and bluetooth technologies

Personal Number (PN) service or Follow-me service allows a user to access telecommunication services with any terminal (e.g., fixed telephones or mobile phones) in any location within the service area. To provide this feature, the PN user needs to manually register with a phone number every time he/she enters a new location. If the user forgets to register the new phone number, the incoming calls will be misrouted. To provide user-friendly follow-me service, this paper proposes an automatic follow-me service (AFS) approach that automatically updates the PN records in the Follow-me database. The significance of our approach is that AFS can be integrated with existing Follow-me databases to automate PIN services offered by different PSTN service providers. We show how AFS can be implemented by using the Voice over IP and Bluetooth technologies. Then, we propose an analytic model to investigate the performance of AFS. The analytic results are validated by simulation experiments. Our study suggests how to select polling frequency to optimize the AFS performance.

Motion detection based on two-piece linear approximation for cumulative histograms of ratio images in intelligent transportation systems

Motion detection is widely used as the key module for extracting moving objects from image sequences in intelligent transportation systems (ITS). In most of the motion detection methods, backgrounds are subtracted from the captured images. This category of methods is called background subtraction. Since standard intensity can be expressed as the multiplication of illumination and reflectance, illumination changes will produce a poor difference image from background subtraction and affect the accuracy of motion detection. In this paper, we use ratio images as the basis of motion detection. To suitably threshold the target images, two-piece linear approximation is proposed for cumulative histograms to prevent the problems in the searching of peaks and valleys in histograms. Experimental results demonstrate that two-piece linear approximation for cumulative histograms performs very well in thresholding the target images. Moreover, the superiority of motion detection based on ratio images over motion detection based on difference images is also depicted in the experiments.