Conference Paper

Detecting Pedestrians by Learning Shapelet Features

DOI: 10.1109/CVPR.2007.383134 Conference: 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 18-23 June 2007, Minneapolis, Minnesota, USA
Source: DBLP


In this paper, we address the problem of detecting pedes- trians in still images. We introduce an algorithm for learn- ing shapelet features, a set of mid-level features. These fea- tures are focused on local regions of the image and are built from low-level gradient information that discriminates be- tween pedestrian and non-pedestrian classes. Using Ad- aBoost, these shapelet features are created as a combina- tion of oriented gradient responses. To train the final classi- fier, we use AdaBoost for a second time to select a subset of our learned shapelets. By first focusing locally on smaller feature sets, our algorithm attempts to harvest more use- ful information than by examining all the low-level features together. We present quantitative results demonstrating the effectiveness of our algorithm. In particular, we obtain an error rate 14 percentage points lower (at10−6 FPPW) than the previous state of the art detector of Dalal and Triggs (1) on the INRIA dataset.

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    • "On observing the object by a camera, it has been difficult to apply to the object detection in real environments because the problem of false detection due to the light is generated. In order to overcome this problem, the robust features based on brightness and edge are recently proposed in computer vision and machine learning [1]–[3]. Speeded Up Robust Features(SURF), Scale-Invariant Feature Transform(SIFT), Bag-of-Features and Histograms of Oriented Gradients(HOG) are proposed as the feature. "
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    ABSTRACT: This paper introduces the method of detecting a route space for an industrial indoor vehicle. The industrial vehicles work in many industrial fields, e.g., a semiconductor production and a car assembly factory. The detection of the route space under illuminant disturbance is an important problem for the industrial vehicle robot. The industrial vehicle has to move to the same areas in a factory. For these works, the usual industrial vehicle, e.g., an automated guided vehicle (AGV), is transported in path line. However, this conventional system of AGV is not a flexible method to change a goal position. On the other hand, a mobile robot, e.g., a wheeled robot, can move without line or rail for the path, and an auto vehicle runs on the urban road. These robots use multi-sensors. However, the industrial vehicle intent to decrease the number of sensors for the cost down. For the reason, we propose a detection of a passable route space for an AGV. We proposed the detection method using a stereo camera in order to move the AGV without path line. Our proposed method is based on the joint extended HOG (Joint-EHOG) and AdaBoost algorithm. EHOG has the robustness for the illuminant disturbance, however, the EHOG is not robust for variable image size. The proposed method has the robustness to the illuminant disturbance and to variable image sizes of the target. The proposed method decreases a number of wrong detection by weighting factors to a weak classifier and combines the many detection areas by the mean shift clustering. The experimental results show the effectiveness of the proposed method for a route space detection and we finally show the future works.
    41st Annual Conference of the IEEE Industrial Electronics Society (IECON2015), Yokohama, Japan; 11/2015
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    • "In [17], they proposed improvement of edge-based feature (e.g. Haar- like[16], shapelets[18], and shape context[19]) by combining color self-similarity and the motion features from optic flow. However, this approach cannot be realized in real-time whose properties are obviously required for traffic safety systems. "
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    ABSTRACT: The paper presents a fine-grained walking activity recognition toward an inferring pedestrian intention which is an important topic to predict and avoid a pedestrian's dangerous activity. The fine-grained activity recognition is to distinguish different activities between subtle changes such as walking with different directions. We believe a change of pedestrian's activity is significant to grab a pedestrian intention. However, the task is challenging since a couple of reasons, namely (i) in-vehicle mounted camera is always moving (ii) a pedestrian area is too small to capture a motion and shape features (iii) change of pedestrian activity (e.g. walking straight into turning) has only small feature difference. To tackle these problems, we apply vision-based approach in order to classify pedestrian activities. The dense trajectories (DT) method is employed for high-level recognition to capture a detailed difference. Moreover, we additionally extract detection-based region-of-interest (ROI) for higher performance in fine-grained activity recognition. Here, we evaluated our proposed approach on " self-collected dataset " and " near-miss driving recorder (DR) dataset " by dividing several activities– crossing, walking straight, turning, standing and riding a bicycle. Our proposal achieved 93.7% on the self-collected NTSEL traffic dataset and 77.9% on the near-miss DR dataset.
    IEEE Intelligent Transportation Systems Conference (ITSC); 09/2015
    • "Object detection is usually a two-phase process, where binary classification is followed by feature extraction from a local 30 image patch. Different image features (Viola and Jones, 2004; Ahonen et al., 2006; Berg and Malik, 2001; Sabzmeydani and Mori, 2007; Cheng et al., 2006) and binary classifiers (Freund, 2001; Breiman, 2001) are available. To our knowledge such an image processing approach has not been applied to automate peak picking in which it 35 could provide a reliable algorithm for the identification of peaks in multidimensional NMR spectra. "
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    ABSTRACT: A detailed analysis of multidimensional NMR spectra of macromolecules requires the identification of individual resonances (peaks). This task can be tedious and time-consuming and often requires support by experienced users. Automated peak picking algorithms were introduced more than 25 years ago, but there are still major deficiencies/flaws that often prevent complete and error free peak picking of biological macromolecule spectra. The major challenges of automated peak picking algorithms is both the distinction of artifacts from real peaks particularly from those with irregular shapes and also picking peaks in spectral regions with overlapping resonances which are very hard to resolve by existing computer algorithms. In both of these cases a visual inspection approach could be more effective than a "blind" algorithm. We present a novel approach using computer vision (CV) methodology which could be better adapted to the problem of peak recognition. After suitable "training" we successfully applied the CV algorithm to spectra of medium sized soluble proteins up to molecular weights of 26 kDa and to a 130 kDa complex of a tetrameric membrane protein in detergent micelles. Our CV approach outperforms commonly used programs. With suitable training data sets the application of presented method can be extended to automated peak picking in multidimensional spectra of nucleic acids or carbohydrates and adapted to solid state NMR spectra. CV-Peak Picker is available upon request from the authors. © The Author (2015). Published by Oxford University Press. All rights reserved. For Permissions, please email:
    Bioinformatics 05/2015; 31(18). DOI:10.1093/bioinformatics/btv318 · 4.98 Impact Factor
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