Search

Traffic signs images when captured in real environment are small in size compared to other objects. Thus, making it difficult to be accurately detected, more so identified. Of recent, the convolutional neural network (CNN) has been making tremendous progress in object detection due to its high accuracy and fast execution. YOLO (You Only Look Once) is an object detection method, which uses the CNN core module and suited for detection of traffic sign in real environment. In this paper, we implemented the YOLOv3 framework for identification of Malaysian traffic sign in real environment. With the inclusion of the spatial pyramid pooling (SPP) in our YOLOv3 framework, it enables the identification of far and small traffic signs in real environment due to pooling of multi-sizes grids of SPP. We presented results of this implementation on our data set of 2000 images of Malaysian traffic sign and the results at 0.5% confidence level are in the range of 90% with a mean average precision of 82.5%, which is promising.

This paper is about analysing the uniqueness of twins by gait biometric. The motivation arises due to twins, having facial similarity may lay difficulties to a video-based recognition system employing face biometric. Gait, a biometric based on the way a person walk, can perhaps be a useful descriptor. Due to the small size data set, classification via leave-one-out cross validation may not be sufficient to test gait’s viability as a descriptor for twins. Thus, this paper proposes a jackknife-like validation in a matched-pair classification. Comparing between the results of both validation approaches, results of the proposed method have shown to be promising. The results perhaps may point to the uniqueness of each individual twin by gait biometric.

This paper presents a classification analysis of gait biometric on twins and non-twins siblings. The aim of this paper is to investigate the existence or inexistence of similarity in the gait of twins and compare it to the gait of non-twins siblings. The motivation behind this paper is that a video-based surveillance system may not be able to rely on face biometric alone when dealing with twins. The features used are the angular displacement walking trajectories of lower limbs. Also this paper proposes a gait cycle normalization task via Bezier polynomial root-finding and re-sampling to ensure a robust analysis against differences in walking speed. Two established classifiers, the linear discriminant analysis (LDA) and k-nearest neighbor are used to classify the data sets of twins and non-twins siblings. Results may indicate that there is similarity in the gait of twins.

In order to determine the appropriate number of thresholds that a grey-level image should be thresholded by so that the resulting image preserves as much information as possible from the original image using the least possible bits, we propose in this paper, a novel criterion for multilevel image thresholding. The criterion is a weighted sum of within-class variance and the number of edge pixels in the thresholded image. To determine the appropriate number of thresholds, an image has to be thresholded iteratively with increasing number of thresholds by any standard thresholding method and the solution that minimizes the proposed criterion is chosen as the appropriate solution. We also present an efficient technique to compute the number of edge pixels. Experiments on a variety of real-world images show that the proposed criterion gives visually more consistent results compared to the most widely used automatic thresholding criterion proposed by Yen et al. (1995).© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

This paper presents a simple model-free gait extraction approach for human identification by using Support Vector Machine. The proposed approach consists of three parts: extraction of human gait features from enhanced human silhouette, smoothing process on extracted gait features and classification by Support Vector Machine (SVM). The gait features extracted are height, width, crotch height, step-size of the human silhouette and joint trajectories. To improve the classification performance, two of these extracted gait features are smoothened before the classification process in order to alleviate the effect of outliers. The proposed approach has been applied on SOTON covariate database, which is comprised of eleven subjects walking bidirectional in a controlled indoor environment with thirteen different covariate factors that vary in terms of apparel, walking speed, shoe types and carrying objects. From the experimental results, it can be concluded that the proposed approach is effective in human identification from a distance.

Gait is another potential human biometrics to look into whenever face recognition fails in video-based systems as is the case with siblings that have similar faces. We perform analyses on 10 pairs of siblings where their faces are assumed to have similarities. Our gait features are the angular displacement trajectories of walking individuals. We apply smoothing with the Bezier polynomial in our root-finding algorithm for accurate gait cycle extraction. Then, we apply classification using two different classifiers; the linear discriminant analysis (LDA) and the k-nearest neighbour (kNN). The best average correct classification rate (CCR) is 100% with a city-block distance kNN classifier. Hence, it is suggested that in the case where face recognition fails, gait may be the better alternative for biometric identification.

Gait as a biometric has received great attention nowadays as it can offer human identification at a distance without any contact with the feature capturing device. This is motivated by the increasing number of synchronised closed-circuit television (CCTV) cameras which have been installed in many major towns, in order to monitor and prevent crime. This paper proposes a new approach for gait classification with twelve different covariate factors. The proposed approach is consisted of two parts: extraction of human gait features from enhanced human silhouette and classification of the extracted human gait features using fuzzy k-nearest neighbours (KNN). The joint trajectories together with the height, width and crotch height of the human silhouette are collected and used for gait analysis. To improve the recognition rate, two of these features are smoothened before the classification process in order to alleviate the effect of outliers. Experimental results of a dataset involving nine walking subjects have demonstrated the effectiveness of the proposed approach.

In this paper, we proposed a new approach for the classification of human gait features with different apparel and various walking speed. The approach consists of two parts: extraction of human gait features from enhanced human silhouette and classification of the extracted human gait features using fuzzy k-nearest neighbours (KNN). The joint angles together with the height, width and crotch height of the human silhouette are collected and used for gait analysis. The training and the testing sets are separable without overlapping. Both sets involve nine different apparel and three walking speed. From the experiment conducted, it can be observed that the proposed system is feasible as satisfactory results have been achieved.

In this paper, a new approach is proposed for extracting human gait features from a walking human based on the silhouette images. The approach consists of six stages: clearing the background noise of image by morphological opening; measuring of the width and height of the human silhouette; dividing the enhanced human silhouette into six body segments based on anatomical knowledge; applying morphological skeleton to obtain the body skeleton; applying Hough transform to obtain the joint angles from the body segment skeletons; and measuring the distance between the bottom of right leg and left leg from the body segment skeletons. The angles of joints, step-size together with the height and width of the human silhouette are collected and used for gait analysis. The experimental results have demonstrated that the proposed system is feasible and achieved satisfactory results.

In this paper, a new approach is proposed for extracting human gait features from a walking human based on the silhouette image. The approach consists of five stages: clearing the background noise of image by morphological opening; measuring the width and height of the human silhouette; dividing the enhanced human silhouette into six body segments based on anatomical knowledge; applying morphological skeleton to obtain the body skeleton; and applying Hough transform to obtain the joint angles from the body segment skeletons. The joint angles together with the height and width of the human silhouette are collected and used for gait analysis. From the experiment conducted, it can be observed that the proposed system is feasible as satisfactory results have been achieved.

Gait as a biometric has received great attention nowadays as it can offer human identification at a distance without any contact with the feature capturing device. This is motivated by the increasing number of synchronised closed-circuit television (CCTV) cameras which have been installed in many major towns, in order to monitor and prevent crime by identifying the criminal or suspect. This paper present a method to improve gait classification results by applying smoothing techniques on the extracted gait features. The proposed approach is consisted of three parts: extraction of human gait features from enhanced human silhouette, smoothing process on extracted gait features and classification by fuzzy k-nearest neighbours (KNN). The extracted gait features are height, width, crotch height, step-size of the human silhouette and joint trajectories. To improve the recognition rate, two of these extracted gait features are smoothened before the classification process in order to alleviate the effect of outliers. The proposed approach has been applied on a dataset of nine subjects walking bidirectionally on an indoor pathway with twelve different covariate factors. From the experimental results, it can be concluded that the proposed approach is effective in gait classification.