Hand Posture Recognition with standard webcam for Natural Interaction

This paper presents an experimental prototype designed for natural human-computer interaction in an environmental intelligence system. Using computer vision resources, it analyzes the images captured by a webcam to recognize a person’s hand movements. There is now a strong trend in interpreting these hand and body movements in general, with computer vision, which is a very attractive field of research. In this study, a mechanism for natural interaction was implemented by analyzing images captured by a webcam based on hand geometry and posture, to show its movements in our model. A camera is installed in such a manner that it can discriminate the movements a person makes using Background Subtraction. Then hands are searched for assisted by segmentation by skin color detection and a series of classifiers. Finally, the geometric characteristics of the hands are extracted to distinguish defined control action positions

Using Haar-like feature classifiers for hand tracking in tabletop augmented reality

We propose in this paper a hand interaction approach to Augmented Reality Tabletop applications. We detect the user’s hands using haar-like feature classifiers and correlate its positions with the fixed markers on the table. This gives the user the possibility to move, rotate and resize the virtual objects located over the table with their bare hands.Postprint (published version

Nhận dạng cử chỉ của bàn tay người theo thời gian thực.

This paper proposes a new  method of hand gesture recognition using Microsoft’s Kinect in real-time. Our system includes detecting and recognizing hand gestures via combining shape, local auto-correlation information and multi-class support vector machine (SVM). Our evaluation shows that the system recognizes one-handed gestures with more than 93% accuracy in real-time. The efficiency of the system execution is good enough and we are encouraged to develop a natural human-machine interaction in the near future.Bài báo trình bày một số kết quả nhận dạng cử chỉ của bàn tay người theo thời gian thực sử dụng thông tin thu được từ cảm biến Kinect của hãng Microsoft. Một số kết quả chính của hướng nghiên cứu được trình bày như: kỹ thuật tách vùng bàn tay, nhận dạng tư thế của bàn tay, đề xuất thuật toán hiệu chỉnh kết quả nhận dạng từ chuỗi các tư thế. Kết quả nhận dạng cho độ chính xác khả quan (trên 93%) tạo tiền đề cho các ứng dụng tương tác người máy theo thời gian thực

Understanding head and hand activities and coordination in naturalistic driving videos

Abstract — In this work, we propose a vision-based analysis framework for recognizing in-vehicle activities such as interac-tions with the steering wheel, the instrument cluster and the gear. The framework leverages two views for activity analysis, a camera looking at the driver’s hand and another looking at the driver’s head. The techniques proposed can be used by researchers in order to extract ‘mid-level ’ information from video, which is information that represents some semantic understanding of the scene but may still require an expert in order to distinguish difficult cases or leverage the cues to perform drive analysis. Unlike such information, ’low-level’ video is large in quantity and can’t be used unless processed entirely by an expert. This work can apply to minimizing manual labor so that researchers may better benefit from the accessibility of the data and provide them with the ability to perform larger-scaled studies. I

A Comparison of Image Processing Techniques for Bird Detection

Orchard fruits and vegetable crops are vulnerable to wild birds and animals. These wild birds and animals can cause critical damage to the produce. Traditional methods of scaring away birds such as scarecrows are not long-term solutions but short-term solutions. This is a huge problem especially near areas like San Luis Obispo where there are vineyards. Bird damage can be as high as 50% for grapes being grown in vineyards. The total estimated revenue lost annually in the 10 counties in California due to bird and rodent damage to 22 selected crops ranged from $168 million to$504 million (in 2009 dollars). A more effective and permanent system needs to be put into place. Monitoring systems in agricultural settings could potentially provide a lot of data for image processing. Most current monitoring systems however don’t focus on image processing but instead really heavily on sensors. Just having sensors for certain systems work, but for birds, monitoring it is not an option because they are not domesticated like pigs, cows etc. in which most these agricultural monitoring systems work on. Birds can fly in and out of the area whereas domesticated animals can be confined to certain physical regions. The most crucial step in a smart scarecrow system would be how a threat would v be detected. Image processing methods can be effectively applied to detecting items in video footage. This paper will focus on bird detection and will analyze motion detection with image subtraction, bird detection with template matching, and bird detection with the Viola-Jones Algorithm. Of the methods considered, bird detection with the Viola-Jones Algorithm had the highest accuracy (87%) with a somewhat low false positive rate. This image processing step would ideally be incorporated with hardware (such as a microcontroller or FPGA, sensors, a camera etc.) to form a smart scarecrow system

HUMAN DETECTION AND TRACKING ENHANCING SECURITY SYSTEMS AT PORTS OF ENTRY

The dissertation undertakes the critical application of establishing smarter surveillance systems to improve security measures in various environments. Human detection and tracking are two image processing methods that can contribute to the development of a smart surveillance system. These techniques are used to identify and detect moving humans in a surveyed area. The research enables the incorporation of personnel detection and tracking algorithms to enhance standard security measures that can be utilized at ports of entry where security is a major hurdle. This system allows authorized operators on any supported console to monitor and receive different alerts levels to indicate human presence.The presented research focuses on two human detectors based on the histogram of oriented gradients detection approach and the Haar-like feature detection approach. According to the conducted experimental results, merging the two detectors, results in a human detector with a high detection rate and lower false positive rate. A novel approach to use both detectors is proposed. This approach is based on a feedback messaging system that inputs parameters from both detectors to output better detection decisions. An object tracker complements the detection step by providing real-time object tracking. An alert system is also proposed to automatically report potential threats occurring in the surveyed area

Analysis of rotational robustness of hand detection with a viola-jones detector

The research described in this paper analyzes the in-plane rotational robustness of the Viola-Jones object detection method when used for hand appearance detection. We determine the rotational bounds for training and detection for achieving undiminished performance without an increase in classifier complexity. The result – up to 15 ° total – differs from the method’s performance on faces (30 ° total). We found that randomly rotating the training data within these bounds allows for detection rates about one order of magnitude better than those trained on strictly aligned data. The implications of the results effect both savings in training costs as well as increased naturalness and comfort of vision-based hand gesture interfaces. 1

Skin texture features for face recognition

Face recognition has been deployed in a wide range of important applications including surveillance and forensic identification. However, it still seems to be a challenging problem as its performance severely degrades under illumination, pose and expression variations, as well as with occlusions, and aging. In this thesis, we have investigated the use of local facial skin data as a source of biometric information to improve human recognition. Skin texture features have been exploited in three major tasks, which include (i) improving the performance of conventional face recognition systems, (ii) building an adaptive skin-based face recognition system, and (iii) dealing with circumstances when a full view of the face may not be avai'lable. Additionally, a fully automated scheme is presented for localizing eyes and mouth and segmenting four facial regions: forehead, right cheek, left cheek and chin. These four regions are divided into nonoverlapping patches with equal size. A novel skin/non-skin classifier is proposed for detecting patches containing only skin texture and therefore detecting the pure-skin regions. Experiments using the XM2VTS database indicate that the forehead region has the most significant biometric information. The use of forehead texture features improves the rank-l identification of Eigenfaces system from 77.63% to 84.07%. The rank-l identification is equal 93.56% when this region is fused with Kernel Direct Discriminant Analysis algorithm