Detecting Pedestrians by Learning Shapelet Features

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 (at 10−6 FPPW) than the previous state of the art detector of Dalal and Triggs [1] on the INRIA dataset. 1

Detecting pedestrians in still images using learned shape features

The problem of detecting pedestrians in images has received much attention from the computer vision community because of its variety of applications. This problem can be considered as a two-class classification problem by labeling windows cropped from the images as pedestrians or non-pedestrians. We present two novel methods for detecting pedestrians in still images. The first method uses coarse shape cues, and is based on a likelihood ratio test. Likelihoods for shape descriptors on pedestrian and non-pedestrian images are obtained using kernel density estimation. In the second approach, we introduce a new method for learning local discriminative features from training examples, and use them for object classification. This method uses two folds of the AdaBoost classifier, first for feature creation and second to train the final classifier. The quantitative results show that the performance of this method is better than the state of the art pedestrian detector

Semi-latent Dirichlet allocation: A hierarchical model for human action recognition

Abstract. We propose a new method for human action recognition from video sequences using latent topic models. Video sequences are represented by a novel “bag-of-words ” representation, where each frame corresponds to a “word”. The major difference between our model and previous latent topic models for recognition problems in computer vision is that, our model is trained in a “semi-supervised ” way. Our model has several advantages over other similar models. First of all, the training is much easier due to the decoupling of the model parameters. Secondly, it naturally solves the problem of how to choose the appropriate number of latent topics. Thirdly, it achieves much better performance by utilizing the information provided by the class labels in the training set. We present action classification and irregularity detection results, and show improvement over previous methods.

Sharif CESR Small Size Robocup Team

Introduction Robotic soccer is a challenD88 research area, which in volves multi leagen ts that ntp to collaboratein an adversarial en viron8] t to achieve s ecific objectives. Here we describe the Sharif CESR small robot team, which was artici ated in Robocu 2001 small size league in Seattle, USA. This a er ex lain s the overall architecture of our robotic soccer system. Figure 1 shows a icture of our soccer robots. Fig. 1. Two robots of Sharif CESR small size robocu team. 2 Mechanics The Sharif CESR team con sists of four iden tical field layersan d a goalkee er. Each robot uses two DC Faulhaber DC motors with a 3.71:1 reduction gear box an twoinW:L+p tal en' ders with resolution of 512 ulses er revolution of motor axis. The algorithm to estimate the velocity from theen# der out ut is A. Birk, S. Coradeschi, and S. Tadokoro (Eds.): RoboCup 2001, LNAI 2377, pp. 595--598, 2002. c # Springer-Verlag Berlin Heidelberg 2002 596 Mohammad Taghi Manzuri et al. implemen ted on the robot