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Traditional vehicle detectors always utilize singletemplate model to represent the vehicle which can not encircle vehicles with different aspect ratios. In this paper, we propose a fast and accurate approach for detecting vehicles which joints classification and aspect ratio regression. The key idea is extending the boosting decision trees method to estimate vehicle's aspect ratio during vehicle detection,...
Detecting pedestrians that are partially occluded remains a challenging problem due to variations and uncertainties of partial occlusion patterns. Following a commonly used framework of handling partial occlusions by part detection, we propose a multi-label learning approach to jointly learn part detectors to capture partial occlusion patterns. The part detectors share a set of decision trees via...
In this paper, we propose an integrated system for scale-variance pedestrian detection. It consists of two cascaded components: a multi-layer detection neural network (MLDNN) for scale-variance pedestrian detection, and a fast decision forest (FDF) for boosting detection performance with only a slight decrease in speed. Experimental results on the Caltech Pedestrian dataset show that our approach...
We aim to study the modeling limitations of the commonly employed boosted decision trees classifier. Inspired by the success of large, data-hungry visual recognition models (e.g. deep convolutional neural networks), this paper focuses on the relationship between modeling capacity of the weak learners, dataset size, and dataset properties. A set of novel experiments on the Caltech Pedestrian Detection...
Hough Forest is a framework combining Hough Transform and Random Forest for object detection. The purpose of the present paper is to improve the efficiency and reliability of the original framework by the mean of two contributions. First, instead of generating the image samples by drawing patches randomly from the training set, we bias this step toward the most relevant image content by selecting...
Email spam is an increasing problem because it disrupting and time consuming for user, since the easy and cheap of sending email. Email Spam filtering can be done with a binary classification with machine learning as classifier. To date, email spam detection still challenging since the email spam still happens a lot and the detection still need improvement. Decision Tree (DT) is one of famous classifier...
In this work, we present a new multiple channel feature called Deep Compact Channel Feature (DCCF), which generates a compact, discriminative feature representation by a pre-trained deep encoder-decoder. With the combination of DCCF and boosted decision trees, a new object detector is proposed which achieved outstanding performance on standard pedestrian dataset INRIA and Caltech. Furthermore, a large...
Face detection algorithm based on a cascade of ensembles of decision trees (CEDT) is presented. The new approach allows detecting faces other than the front position through the use of multiple classifiers. Each classifier is trained for a specific range of angles of the rotation head. The results showed a high rate of productivity for CEDT on images with standard size. The algorithm increases the...
T3D edge detection from a depth image is an important technique of 3D object recognition in preprocessing. There are three types of 3D edges in a depth image called jump, convex roof, and concave roof edges. Conventional 3D edge detection based on ring operators has been proposed. The conventional ring operator can detect three types of 3D edges by classifying the response of Fourier transforms. Since...
The aim of this paper is to present the algorithms that were developed for detecting human in the conditions of overlapping and non-overlapping. Overlapping means when person is not fully visible in the image and occluded by another person at the front or right/left side. In order to achieve this goal, three steps were implemented. The algorithms were implemented in C++ with the help of Open Source...
Corner detection is a important task in low level vision. Detecting corners helps one to establish similarity between two or more images. Traditional approaches for corner detection involve finding significant variation around a pixel neighbourhood in two different directions. In this work, we have developed a novel framework to detect corners in a given image by learning corners from images corresponding...
This paper presents a novel approach for pedestrian detection using oriented line scans of gradients computed from a gray level image. Three feature types are proposed that can be generated easily from oriented gradients and an effective use of integral lines and integral images. A scalable cascaded classifier is built by combining oriented gradients with the oriented line scan features in a boosting...
We present a new pedestrian detector that improves both in speed and quality over state-of-the-art. By efficiently handling different scales and transferring computation from test time to training time, detection speed is improved. When processing monocular images, our system provides high quality detections at 50 fps. We also propose a new method for exploiting geometric context extracted from stereo...
We consider the problem of robotic object detection of such objects as mugs, cups, and staplers in indoor environments. While object detection has made significant progress in recent years, many current approaches involve extremely complex algorithms, and are prohibitively slow when applied to large scale robotic settings. In this paper, we describe an object detection system that is designed to scale...
Pedestrian detection in a real scene is an interesting application for video surveillance systems. This paper presents our contribution to improve the work of Viola and Jones, originally designed to detect faces. This work uses a cascade of classifiers based on Adaboost using Haar features. It improves the learning step by including a decision tree presenting the different poses and possible occlusions...
In this paper, we describe a robust object detection method using decision trees and a new cascade architecture. On the one hand, we design a weak classifier for multi-valued features on AdaBoost algorithm based on decision trees method, which directly reduces training time and increases the object detectionpsilas precision. On the other hand, the use of new cascade architecture is great helpful for...
One of the core components of any visual surveillance system is object classification, where detected objects are classified into different categories of interest. Although in airports or train stations, abandoned objects are mainly luggage or trolleys, none of the existing works in the literature have attempted to classify or recognize trolleys. In this paper, we analyzed and classified images of...
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