-level classifier on the excitation of these part detectors. In this study we develop a hierarchical detection architecture for automobiles. The classifier operates by first locating keypoints in the test image with a well known interest operator. These keypoints are then compared against a corpus of car

Recently, Viola and Jones [1] have proposed a detector using Adaboost to select and combine weak classifiers from a very large pool of weak classifiers, and it has been proven to be very successful for detecting faces. We have followed their approach and applied it to detect rear views of cars

used to measure autonomic nervous system activation. The signals were digitized in real time and stored on the SmartCar's pentium class computer. Each drive followed a prespecified route through fifteen different events, from which four stress level categories were created according

A car detection system based on hierarchical visual features

We present a system to detect passenger cars in aerial images wherecars appear as small objects. We pose this as a 3D object recognition problem to account for the variation in viewpoint and the shadow. We startedfrom psychological tests to find important features for human detection of cars. Based

In this paper we present a novel fast multi-cues based car detection technique in still outdoor images. On the bottom level, two novel area templates based on edge cue and interest points cue are first designed, which can rapidly reject most of the non-car sub-windows at the cost of missing few

detection task is finding adequate features which are capable to separate cars from other objects; especially those that look similar. We present an experiment where selected features show their ability of car detection. Precisely, Haar-like and HoG features are utilized and passed to the AdaBoost algorithm

This paper describes a general, trainable architecture for object detection that has previously been applied to face and people detection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which

approaches that use Support Vector Machines (SVMs). Finally, we present the design and implementation of a system to locate cars based on the detections of human-specified components.

Car detection from aerial images has been studied for years. However, given a large-scale aerial image with typical car and background appearance variations, robust and efficient car detection is still a challenging problem. In this paper, we present a novel and robust framework for automatic car