Limin Meng

An ROIs based pedestrian detection system for single images

Pedestrian detection in a single image takes more time than that in a video due to the requirement of scanning the whole image. In this paper, an improved pedestrian detection system based on region of interest (ROI) is proposed for single images. In the improved pedestrian detection system, principal component analysis (PCA) is introduced to improve the detection rate and accuracy of histogram of oriented gradients (HOG) based support vector machine (SVM) classifier for pedestrian detection. PCA eliminates the redundant HOG feature dimensions that have no contribution for the pedestrian classification. A novel ROI extraction method based on fuzzy C-means (FCM) clustering algorithm is used to select the regions that possibly contain pedestrians in a single image. ROI extraction reduces the number of detection windows, resulting in a significant reduction in detection time of a single image. Computer experiments show that the proposed pedestrian detection system can correctly detect the positions of pedestrians in single images in real time.

A design of degree distribution for systematic fountain codes

This paper proposes a new degree distribution for systematic fountain codes by analyzing the property of systematic code. The erasure probabilities are also taken into consideration during the design. The simulation shows that the new distribution needs less encoding packets to recover all the source packets, has rapid constringency speed, and its performance is not very sensitive to the erasure probabilities.

An improved sampling strategy for randomized hough transform based line detection

Detecting lines correctly from a digital image is an important step in many real-world applications. It has been widely used in the fields of contour extraction, character recognition and medical image analysis, as well as in many other computer vision based applications. In this paper, we present a randomized Hough transform based line detection algorithm that utilizes the edge gradient direction. This method exploits edge gradient direction to determine the main direction of a line by applying a constraint on the randomized Hough transform. It substantially reduces the count of invalid samples in the random sampling process. The proposed sampling strategy is superior to some existing methods in terms of memory requirement and computation time.

A machine learning approach to urban traffic state detection

We propose an urban traffic state detection method based on support vector machine (SVM) and multilayer perception (MLP). Fusing the SVM and MLP classifiers into a cascade two-tier classifier improves the accuracy of the traffic state classification. A cascade two-tier classifier MLP-SVM, a single SVM classifier and a single MLP classifier are then fused to further improve the final detection accuracy. We also implement a Dempster-Shafer (D-S) theory of evidence based classifier. Finally, fusion strategies at the training and implementation phases are proposed to improve the detection accuracy.

A video-based traffic violation detection system

Traffic violation detection systems are effective tools to help traffic administration to monitor the traffic condition. It can detect traffic violations, such as running red lights, speeding, and vehicle retrogress in real time. In this paper, we propose an improved background-updating algorithm by using wavelet transform on dynamic background, and then track moving vehicles by feature-based tracking method. A complete traffic violation detection system is realized in C++ with OpenCV.

A novel algorithm for license plate location based on the RGB features and the texture features

In order to locate license plates in a complex environment, we develop a novel license plate location algorithm by combing the texture features of plate and features in the RGB color space. A picture is first filtered with a custom convolutional kernel in order to enhance the texture of characters, followed by morphological processing to get region set A. The RGB features of the license plate are then extracted from the picture, and region set B is obtained by performing morphological processing. Region set C is obtained by performing intersection operation of A and B. Finally, through elimination of pseudo-regions, the real license plate region is located. Evaluation of this algorithm on pictures sampled from different illuminations and road conditions demonstrates an accuracy of 98%.

A randomized circle detection method with application to detection of circular traffic signs

We propose an improved randomized circle detection method. The improved method reduces the computational complexity of the randomized circle detection method by a factor of two. We then apply the proposed method to detection of circular traffic signs. For traffic images taken in complex scenarios, the colors of interest are first segmented, obtaining potential regions of traffic signs. By applying edge detection and improved randomized circle detection method, traffic signs can be exactly located. Experimental results show that the proposed method has a small computational requirement for natural scenes under different lighting conditions and it can fast and accurately locate circular traffic signs. It can also position circular traffic signs with occlusions and variations in shape, size, and color.