In industrial sites, many industrial accidents cause human casualties every year, and deep learning-based object detection and danger zone management technologies are being proposed to minimize accidents. However, existing studies have focused only on object detection which has poor performance when a dangerous situation occurs by interacting two or more accident-causing objects. This paper proposes a method that detects accident risks in advance through object detection and risk interaction analysis between objects. It consists of four modules: image acquisition, object detection, worker action analysis, and risk event detection by dangerous object interaction. YOLOv4 is selected and fine-tuned to detect workers and conveyor objects that cause accidents by interacting objects. After selecting the danger and caution zones, it determines whether or not the detection object exists around the danger zone. A total of 68,621 image datasets collected from industrial sites were created to train and evaluate the system. The mAP of 91.79% for object detection is obtained and the F1 score of 88.9% for risk event detection is obtained.

A horseback riding simulator is a robotic machine that simulates the motion of horseback riding. In this paper, we present an interaction control system for a horseback riding simulator. The proposed system provides a postural correction function suited to the user based on their historical log and specialized posture coaching data. The system has adopted certain schemes for posture detection and recognition of the identified user as a way to recommend customized exercise modes. Our experiments show that including these techniques will help users maintain good posture while riding.

IT technologies have started to be applied to many fields such as sports science, robotics or BT. Especially, object detection and recognition using vision sensor has intensively studied due to diverse application fields. But, various illumination condition, pose or time progress have put object detection and recognition to challenge task in the real world. In this paper, face detection and recognition using vision sensor applied to sports simulator which has been proposed. Face detection has been processed to identify straight in the face that has used to detect head pose of riders. Face recognition has used to identify user, who has tried to take horse riding simulator. Gabor wavelet and face graph has used to recognize low quality face image, which has acquired under poor illumination environment and movement of simulator. We have simulated on FERET and ETRI database. ETRI database has acquired on horse riding simulator under poor illumination condition. The accuracy of 91% face recognition rate on FERET and encouraging result of 82% recognition rate on ETRI DB have been obtained.

We propose a user pose verification technique using depth information to compare their pose with expert riders. Xtion sensor by Asus is used for gathering a depth data in real world. The user pose verification algorithm is divided into two categories: user segmentation and user pose verification. In user segmentation step, body parts are segmented based on the region growing algorithm from a head point (i.e. seed point). Then, a simple algorithm is used to generate skeletal joints in the segmented body parts. Finally, the user pose is investigated by the standard pose of experts in the same situation.

This paper proposes user segmentation algorithm using moving edge detection to provide customized coaching service to users in advanced sports simulator like horse riding. To give this service in the simulator, exact user segmentation is very important. If the segmenting user from its background became better, we will get a better pose recognition results. In this paper, some of the well-known object segmentation methods to be applied to sports simulator have been investigated. Also, a moving object detection algorithm for recognizing user's poses which is a novel innovation to assure high recognition reliability in sports simulator has been proposed.

This paper describes a new robot photographer system which can interact with people. The goal of this research is to make the system act like a human photographer. This system is based on a mobile robot having capabilities of wireless communication and stereo vision. It recognizes waving hands of people, moves toward them, and takes pictures with designated compositions. The pictures are transmitted to personal computers over the wireless network. In comparison with previous researches, the most unique things of this system are human interaction and user preference. To realize these properties, this new robot photographer system is considered optical property of lens and applied interesting vision algorithm which was never tried before for previous robot photographer systems

This paper describes camera based character recognition system, which is implemented for mobile devices such as PDA and cellular phones with color cameras. First, we have developed camera based character recognition system for PC that includes techniques such as image enhancement, local adaptive binarization and blob coloring to effectively extract character region and remove the noise of camera captured images. Then, we converted the PC based OCR system to the embedded OCR system for cellular phones. Several functions were specially developed since most of mobile telecommunication devices have no real number computing functions and have limitation of memory space. In this paper, we are addressing the related problems and our system mechanisms.

This paper presents recognition of unconstrained handwritten numeral strings using a decision value generator. The numeral string recognition system is composed of three modules: pre-segmentation, segmentation and recognition. The pre-segmentation module classifies a numeral string into sub-images, such as isolated digits, touching digits or broken digits, based on the confidence value of decision value generator. The segmentation module splits the touching digits using the reliability value of decision value generator. Both segmentation-based and segmentation free methods are used in classification and segmentation. To evaluate the proposed method, experiments were conducted using the handwritten numeral strings of NIST SD19 and a higher recognition performance than previous works was obtained.

A sophisticated methodology of legal amount recognition based on the word segmentation hypotheses is introduced for automatic bank check processing. Word segmentation hypotheses are derived according to the grapheme level segmentation results of the legal amount. Novel hybrid schemes of HMM-MLP classifiers are also introduced for producing the ordered legal word recognition results with reliable decision values. These values can be used for obtaining an optimal word segmentation path of over-segmentation hypotheses as well as an efficient rejection criterion of word recognition result. Simulation was performed with CENPARMI bank check database and shows quite encouraging results.

A methodology of combining HMM (hidden Markov model) and MLP (multilayer perceptron) for cursive word recognition is presented in this paper. We have designed an explicit segmentation based HMM, and combined it with an implicit segmentation based MLP using weighting coefficients. The main idea of this methodology is that more distinct classifiers can better complement each other. We also introduced a new probability measure for the hybrid classifier as well as conventional combining schemes. Experiments were conducted with month word and legal word databases of CENPARMI and improved performances of 87.3% for 21 month word classes and 92.2% for 32 legal word classes have been achieved.

Describes a scheme for recognizing unconstrained handwritten numeral strings by a composite segmentation method which combines both recognition-free and recognition-based segmentation methods. A digit group detector has been designed to separate touching digits from isolated digits by the recognition-free segmentation method. Subsequently touching digits are segmented by prioritized segmentation points accomplished by analyzing the ligature and touching types. Four special kinds of candidate segmentation points and six touching types are defined to obtain more stable segmentation points. To evaluate the proposed method, we have experimented with 1,500 numeral strings of the NIST SD19 database and obtained a recognition rate of 91.8%.

The last frontiers of handwriting recognition are considered to have started in the last decade of the second millennium. The paper summarizes (a) the nature of the problem of handwriting recognition, (b) the state of the art of handwriting recognition at the turn of the new millennium, and (c) the results of CENPARMI researchers in automatic recognition of handwritten digits, touching numerals, cursive scripts, and dates formed by a mixture of the former 3 categories. Wherever possible, comparable results have been tabulated according to techniques used, databases, and performance. Aspects related to human generation and perception of handwriting are discussed. The extraction and usage of human knowledge, and their incorporation into handwriting recognition systems are presented. Challenges, aims, trends, efforts and possible rewards, and suggestions for future investigations are also included.