Showing papers on "Histogram of oriented gradients published in 2006"
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01 Oct 2006TL;DR: Two different methods for vehicle detection in real world situations: Principal Component Analysis and the Histogram of Gradients principle are described and their detection capabilities as well as advantages and disadvantages are compared.
Abstract: The efficient monitoring of traffic flow as well as related surveillance and detection applications demand an increasingly robust recognition of vehicles in image and video data. This paper describes two different methods for vehicle detection in real world situations: Principal Component Analysis and the Histogram of Gradients principle. Both methods are described and their detection capabilities as well as advantages and disadvantages are compared. A large sample dataset which contains images of cars from the backside and frontside in day and night conditions is the basis for creating and optimizing both variants of the proposed algorithms. The resulting two detectors allow recognition of vehicles in frontal view +- 30 deg and views from behind +- 30 deg. The paper demonstrates that both detection methods can operate effectively even under difficult lighting situations with high detection rates and a low number of false positives.
6 citations
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02 Oct 2006TL;DR: In this paper, the authors used histogram of oriented gradients (HOG) and support vector machine (SVM) classifiers for pedestrian detection in real-time applications.
Abstract: State of the art algorithms for people or vehicle detection should not only be accurate in terms of detection performance and low false alarm rate, but also fast enough for real time applications. Accurate algorithms are usually very complex and tend to have a lot of calculated features to be used or parameters available for adjustments. So one big goal is to decrease the amount of necessary features used for object detection while increasing the speed of the algorithm and overall performance by finding an optimum set of classifier variables. In this paper we describe algorithms for feature selection, parameter optimisation and pattern matching especially for the task of pedestrian detection based on Histograms of Oriented Gradients and Support Vector Machine classifiers. Shape features were derived with the Histogram of Oriented Gradients algorithm which resulted in a feature vector of 6318 elements. To decrease computation time to an acceptable limit for real-time detection we reduced the full feature vector to sizes of 1000, 500, 300, 200, and 160 elements with a genetic feature selection method. With the remaining features a Support Vector Machine classifier was build and its classification parameters further optimized to result in less support vectors for further improvements in processing speed. This paper compares the classification performance, of the different SVM's on real videos (some sample images), visualizes the chosen features (which histogram bins on which location in the image search feature) and analyses the performance of the final system with respect to execution time and frame rate.
3 citations
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02 Oct 2006TL;DR: A close to real-time scale invariant implementation of a pedestrian detector system which is based on the Histogram of Oriented Gradients (HOG) principle which is especially suited for very busy and crowded scenarios.
Abstract: This paper describes a close to real-time scale invariant implementation of a pedestrian detector system which is based on the Histogram of Oriented Gradients (HOG) principle Salient HOG features are first selected from a manually created very large database of samples with an evolutionary optimization procedure that directly trains a polynomial Support Vector Machine (SVM) Real-time operation is achieved by a cascaded 2-step classifier which uses first a very fast linear SVM (with the same features as the polynomial SVM) to reject most of the irrelevant detections and then computes the decision function with a polynomial SVM on the remaining set of candidate detections Scale invariance is achieved by running the detector of constant size on scaled versions of the original input images and by clustering the results over all resolutions The pedestrian detection system has been implemented in two versions: i) fully body detection, and ii) upper body only detection The latter is especially suited for very busy and crowded scenarios On a state-of-the-art PC it is able to run at a frequency of 8 - 20 frames/sec
3 citations
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01 Apr 2006
TL;DR: A system for detecting pedestrians using histogram of oriented gradients by means of support vector machines to develop a system that combines the advantages of using far infrared or daylight technologies.
Abstract: : This report presents the research activities within the framework of N62558-05-P-0380 contract for the development of a Human Shape localization system by means of a 4-camera vision system consisting of 2 daylight and 2 far infrared cameras. The main idea is to exploit the advantages of both far infrared and visible cameras to develop a system that combines the advantages of using far infrared or daylight technologies. In particular, this report details a system for detecting pedestrians using histogram of oriented gradients by means of support vector machines.
1 citations