Head Detection and Tracking for an Intelligent Room

We present a novel feature extraction method, which employs a histogram of transition feature, as an input to a SVM classifier. This feature relies on foreground extraction. We also evaluate some foreground extraction method. To evaluate the performance of this feature, we use it for head detection. Then, by applying a combination of the Harris corner detector and Lucas-Kanade tracker and motion pattern, we track the head position. The performance of the proposed method is experimentally shown.SICE Annual Conference 2014 - International conference on Instrumentation, Control, Information Technology and System Integration, September 9-12, 2014, Hokkaido University, Sapporo, Japa

Detecting a Taxi from a Video for Visually Handicapped People

This paper proposes a method of detecting a specific moving object, a taxi in particular, on a road from a video provided from a camera attached to a user. In order to raise the quality of life of visually handicapped people, a computer vision system which works in place of their eyes and a brain may be useful. As one of such systems, this study focuses its attention on finding a taxi on a road which is a convenient vehicle to such people as a means of transfer outdoors. The novel idea of this study is that a camera and a PC system for finding a taxi is carried by a user, a visually handicapped person, for example. The proposed method employs the HOG features to represent a vehicle, and finds a taxi by Real AdaBoost and color information with the detected vehicle. The performance of the proposed method is shown experimentally.The 34th Chinese Control Conference and SICE Annual Conference 2015, July 28-30, 2015, Hangzhou, Chin

A COLOR FEATURES-BASED METHOD FOR OBJECT TRACKING EMPLOYING A PARTICLE FILTER ALGORITHM

We proposed a method for object tracking employing a particle filter based on color feature method. A histogram‐based framework is used to describe the features. Histograms are useful because they have property that they allow changes in the object appearance while the histograms remain the same. Particle filtering is used because it is very robust for non‐linear and non‐Gaussian dynamic state estimation problems and performs well when clutter and occlusions are present on the image. Bhattacharyya distance is used to weight the samples in the particle filter by comparing each sample’s histogram with a specified target model and it makes the measurement matching and sample’s weight updating more reasonable. The method is capable to track successfully the moving object in different outdoor environment with and without initial positions information, and also, capable to track the moving object in the presence of occlusion using an appearance condition. In this paper, we propose a color features‐based method for object tracking based on the particle filters. The experimental results and data show the feasibility and the effectiveness of our method.International Conference on Power Control and Optimization, 1-3, June 2009, Bali, Indonesi

Abnormal motion detection in an occlusion environment

We present a motion classification approach to detect movements of interest (abnormal motion) based on optical flow. By tracking all feature points of a moving human in successive frames, we calculate the coordinate space and create feature space. This is done directly from the intensity information without explicitly computing the underlying motions. It requires no foreground segmentation, no prior learning of activities, no motion recognition and no object detection. First, we determine the abnormal scene and speed by using the velocity histogram. Then by using k-means clustering over velocity orientation and magnitude, we determine the abnormal direction. The performance of the proposed method is experimentally shown.SICE Annual Conference 2013 - International conference on Instrumentation, Control, Information Technology and System Integration September 14-17, 2013, Nagoya University, Nagoya, Japa

Multiple-window Bag of Features for Road Environment Recognition

The idea of Bag of Features (BoF) is recently often employed for general object recognition. But, as it does not take positional relations of detected features into account, the recognition rate is still not very high for practical use. This paper proposes a method of describing the feature of an object by the BoF representation which considers positional information of the features. Although the original BoF representation is applied to an entire image, the proposed method employs multiple windows on an image. The BoF representation is applied to each of the windows to represent an object in the image interested for recognition. The performance of the proposed method is shown experimentally

Classifying seabed sediments using local auto-correlation features

Understanding the distribution of seafloor sediment using a side-scan sonar is very important to grasp the distribution of seabed resources. This task is traditionally carried out by a skilled human operator. However, with the appearance of Autonomous Underwater Vehicles, automated processing is now needed to tackle the large amount of data produced and to enable on the fly adaptation of the missions and near real time update of the operator. We propose in this paper a method that applies a higher-order local auto-correlation feature and a subspace method to the acoustic image provided by the side-scan sonar to classify seabed sediment automatically. In texture classification, the proposed method outperformed other methods such as a gray level co-occurrence matrix and a Local Binary Pattern operator. Experimental results show that the proposed method produces consistent maps of a seafloor

A Visualization System of Scaler Stroke Motion

Periodontitis is a dental disease from which many people suffer. The most effective treatment with it is to remove dental plaque and scale periodically by a scaler. For this purpose, those who wish to be a dentist or a hygienist must take training of scaling and root planing using a jaw model and a scaler. It is, however, difficult for a trainer to evaluate the scaler stroke motion of a trainee, since the end of the scaler in a mouth cannot be observed directly from out of the mouth. This paper proposes a novel method of visualizing the scaler stroke motion in the mouth three-dimensionally by the employment of a camera and a computer. The system is described and an experimental result is shown

A pedestrian detection method using the extension of the HOG feature

Development of an ITS (Intelligent Transport System) has drawn much attention from computer vision community in recent years. In particular, various techniques for detecting pedestrians automatically have been proposed by many researchers. Among them, the HOG feature proposed by Dalai & Triggs has gained much interest in the pedestrian detection. However, previous methods including the original HOG feature have not achieved satisfactory detection rates. In this paper, we propose an extension of the HOG feature, i.e., flexible choice of the number of bins and automatic definition of a cell size and a block size by parameterizing their scales. By comparative experiments, it was confirmed that the proposed method outperforms the previous methods in the performance of pedestrian detection.SCIS & ISIS 2014, December 3-6, 2014, Kitakyushu International Conference Cente