Vision based Traffic Police Hand Signal Recognition in Surveillance Video- A Survey R.Sathya <s> Sign Language Gestures <s> A gesture recognition method for Japanese sign language is presented. We have developed a posture recognition system using neural networks which could recognize a finger alphabet of 42 symbols. We then developed a gesture recognition system where each gesture specifies a word. Gesture recognition is more difficult than posture recognition because it has to handle dynamic processes. To deal with dynamic processes we use a recurrent neural network. Here, we describe a gesture recognition method which can recognize continuous gesture. We then discuss the results of our research. <s> BIB001 </s> Vision based Traffic Police Hand Signal Recognition in Surveillance Video- A Survey R.Sathya <s> Sign Language Gestures <s> The sign language is a method of communication for the deaf-mute. Articulated gestures and postures of hands and fingers are commonly used for the sign language. This paper presents a system which recognizes the Korean sign language (KSL) and translates into a normal Korean text. A pair of data-gloves are used as the sensing device for detecting motions of hands and fingers. For efficient recognition of gestures and postures, a technique of efficient classification of motions is proposed and a fuzzy min-max neural network is adopted for on-line pattern recognition. <s> BIB002 </s> Vision based Traffic Police Hand Signal Recognition in Surveillance Video- A Survey R.Sathya <s> Sign Language Gestures <s> We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American sign language (ASL) using a single camera to track the user's unadorned hands. The first system observes the user from a desk mounted camera and achieves 92 percent word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98 percent accuracy (97 percent with an unrestricted grammar). Both experiments use a 40-word lexicon. <s> BIB003 </s> Vision based Traffic Police Hand Signal Recognition in Surveillance Video- A Survey R.Sathya <s> Sign Language Gestures <s> A large vocabulary sign language interpreter is presented with real-time continuous gesture recognition of sign language using a data glove. Sign language, which is usually known as a set of natural language with formal semantic definitions and syntactic rules, is a large set of hand gestures that are daily used to communicate with the hearing impaired. The most critical problem, end-point detection in a stream of gesture input is first solved and then statistical analysis is done according to four parameters in a gesture: posture, position, orientation, and motion. The authors have implemented a prototype system with a lexicon of 250 vocabularies and collected 196 training sentences in Taiwanese Sign Language (TWL). This system uses hidden Markov models (HMMs) for 51 fundamental postures, 6 orientations, and 8 motion primitives. In a signer-dependent way, a sentence of gestures based on these vocabularies can be continuously recognized in real-time and the average recognition rate is 80.4%,. <s> BIB004 </s> Vision based Traffic Police Hand Signal Recognition in Surveillance Video- A Survey R.Sathya <s> Sign Language Gestures <s> The objective of this paper is to introduce the use of two different recurrent neural networks in human hand gesture recognition for static images. Because neural networks are a promising tool for many human computer interaction applications, this paper focuses on the ability of neural networks to assist in Arabic Sign Language(ArSL) hand gesture recognition. We have introduced the steps of our proposed system and have presented the Elmanpsilas model as a partially recurrent architecture and a fully connected network with recurrent links that is believed to help the network to converge and gain stability, then we have tested it in an experiment held for this; the results of the experiment have showed that the suggested system with the fully recurrent architecture has had a performance with an accuracy rate 95%. <s> BIB005 </s> Vision based Traffic Police Hand Signal Recognition in Surveillance Video- A Survey R.Sathya <s> Sign Language Gestures <s> We propose a method to recognize Chinese traffic police gesture in complex scene for intelligent vehicle. The gesture recognition is made by integrated nonparametric background modeling with human pose estimation. Firstly, dark channel prior and kernel density estimation are used to extract police's torso and arms from complex traffic environment as foreground region. Then, the coordinates of pixels in the upper and lower arms are determined by using max-covering scheme, which is based on a key observation that body part tiles maximally cover the foreground region and satisfy body plan. Finally, some typical police gestures can be recognized by rotation joint angle. The experimental results show that this method can obtain favorable results on a number of gesture sequences of traffic police. <s> BIB006
In vision based hand gesture recognition, hand shape segmentation is one of the toughest problems under a dynamic environment. It can be simplified by using visual marking on the hands. Some researchers have implemented sign language and pointing gesture recognition based on different marking modes BIB006 . American sign language gestures are recognized BIB003 . It is one example that has received significant attention in the gesture literature. Korean sign languages BIB002 are recognized and a new gesture recognition algorithm for Korean scripts. BIB004 Taiwanese sign language (TWL) introduced lexicon of 250 vocabularies. Arabic sign language (ArSL) are regonized and classified BIB005 . Japanese sign language BIB001 recognized for words and alphabets, they could recognize 10 words and 42 alphabets using two types of neural network algorithms.