Edgelet Features Research Articles

Research and Design on Vehicle Pedestrian Detection in Natural Scene

We propose a two-layer pedestrian feature extraction algorithm based on multi features fusion in integral channel, which is gained by intelligent driving system in natural environment. The algorithm extracts the gradient direction, gradient magnitude and LUV color channel of the image as upper layer feature, by using the fast operation speed of integral channel. Edgelet features that describe pedestrian local information are used as lower level features for validation. While ensuring real-time detection, we improved the low robustness of single layer features. Experimental results show the algorithm can reduce the false detection rate to a great extent under the premise of ensuring the detection speed in dealing with pedestrians in the background of natural and complicated vehicles.

A Pedestrian Detection Method Based on Hierarchical Tree Cascade Classification at Nighttime

AbstractIllumination, for pedestrian detection at nighttime is weak, and detection is easily affected through variations in illumination. Thus, a bicharacteristic method of pedestrian detection at nighttime based on hierarchical tree cascade classification is presented according to “coarse-to-fine” principle. The proposed method consists of two stages of cascade classifiers. Coarse cascade classifiers are constructed in complete binary tree architecture. These classifiers use Haar-like features for the rapid identification of candidate pedestrian areas. By contrast, fine cascade classifiers have a parallel structure. Edgelet features are used for detection along three parts: the head-shoulder, trunk, and leg parts of candidate pedestrian areas. Bayesian decision-making is adopted to achieve pedestrian target detection and a comprehensive analysis of the detection results from these three parts. Experiments show that the proposed method has high accuracy, ideal real-time performance, and strong reliability...

Vision Based Pedestrian Detection for Advanced Driver assistance

In pedestrian detection intricate feature descriptors are used to improve the detection rate at the cost of computational complexity. In this paper, we propose a detector based on simple, robust edgelet features to enhance the detection rate and classifier based on k-means clustering approach to reduce computational complexity. The proposed framework consists of extraction of candidate features of pedestrian detection using edgelet features and use of the cascade structure of k-means clustering for classification enabling high detection accuracy at low false positives. Experimental results show that the proposed method requires less processing time per frame, making it suitable for real-time systems.

Strip Features for Fast Object Detection

This paper presents a set of effective and efficient features, namely strip features, for detecting objects in real-scene images. Although shapes of a specific class usually have large intraclass variance, some basic local shape elements are relatively stable. Based on this observation, we propose a set of strip features to describe the appearances of those shape elements. Strip features capture object shapes with edgelike and ridgelike strip patterns, which significantly enrich the efficient features such as Haar-like and edgelet features. The proposed features can be efficiently calculated via two kinds of approaches. Moreover, the proposed features can be extended to a perturbed version (namely, perturbed strip features) to alleviate the misalignment caused by deformations. We utilize strip features for object detection under an improved boosting framework, which adopts a complexity-aware criterion to balance the discriminability and efficiency for feature selection. We evaluate the proposed approach for object detection on the public data sets, and the experimental results show the effectiveness and efficiency of the proposed approach.

Boundary extraction using supervised edgelet classification

Traditional learning-based boundary extraction algorithms clas- sify each pixel edge separately and then get boundaries from the local decisions of a classifier. However, we propose a supervised learning method for boundary extraction by using edgelets as boundary elements. First, we extract edgelets by clustering probabilities of boundary. Second, we use features of edgelets to train a classifier that determines whether an edgelet belongs to a boundary. The classifier is trained by utilizing edgelet features, including local appearance, multiscale features, and global scene features such as saliency maps. Finally, we use the classifier to decide the probability that the edgelet belongs to the boundary. The experimental results in the Berkeley Segmentation Dataset demonstrate that our algorithm can improve the performance of boundary extraction. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). (DOI: 10.1117/ 1.OE.51.1.017002)

Segmentation of objects in a detection window by Nonparametric Inhomogeneous CRFs

This paper presents a method for segmenting objects of a specific class in a given detection window. The task is to label each pixel as belonging to the foreground or the background. We pose the problem as that of finding the maximum a posterior (MAP) estimation in a modified form of Conditional Random Field model that we call a Nonparametric Inhomogeneous CRF (NICRFs). An NICRF, like a conventional CRF, has nodes representing pixels and pairwise links connecting neighboring pixels; however, both the unary and pairwise energy terms are inhomogeneous in the sense of being dependent on pixel positions to account for prior information of the known object class. It differs from earlier methods in that position information is in form of unique term functions for each individual pixel, rather than the same parametric function but with varying parameters. Unary terms are given by a learned boosted classifier based on novel Adaptive Edgelet Features (AEFs) for inferring probability of a pixel being foreground; pairwise terms are learned by joint probabilities for neighboring pixels as a function of contrast; a monotonicity constraint is used to reduce possible over-fit effects. We expand the neighborhood used for pairwise terms, and add inhomogeneous weighting factors for different pairwise terms. We use the Loopy Belief Propagation (LBP) algorithm for MAP estimation. A local search process is proposed to deal with inaccurate detection windows. We evaluate our approach on examples of pedestrians and cars and demonstrate significant improvements compared to earlier methods.

Multi Features Combination for Pedestrian Detection

In this paper, we propose a new approach for pedestrian detection in crowded scene from static images. The method is based on hybrid features, one type of middle-level features, which compose of multi features include gradient features, Edgelet features and haar-like features, three low-level feature sets. The gradient features focus on the local point information, the Edgelet features focus on the local edge information and the haar-like features focus on the local region information of the image. We use two stages of Adaboost to train the final classifier. In the first stage, the whole image is divided into many small windows which all include numerous low-level features. Adaboost is used in each window to get one middle-level feature which composes of some best features including gradient features, Edgelet features and haar-like features in this window. Secondly, from all middle-level features, Adaboost is used again to get the final classifier. Experiment results on common datasets and comparisons with some previous methods are given.