Subhash Chand Agrawal

Recognition of Indian Sign Language using feature fusion

Sign Language is the most natural and expressive way for the hearing impaired. This paper presents a method for automatic recognition of two handed signs of Indian Sign Language (ISL). The method consists of three phases: Segmentation, Feature Extraction and Recognition. The segmentation is done through Otsus algorithm. In the feature extraction phase, shape descriptors, HOG descriptors (Histogram of Oriented Gradient) and SIFT (Scale Invariant Feature Transform) feature have been fused to compute a feature vector. In the recognition phase, a multi-class Support Vector Machine (MSVM) is used for training and classifying signs of ISL. The experimental results provide evidence of the effectiveness of the proposed approach with 93% recognition rate.

Redundancy removal for isolated gesture in Indian sign language and recognition using multi-class support vector machine

Sign language is a formal language used by the deaf and dumb people to communicate through bodily movement, especially of hands rather than speech. In this paper, we have presented a vision-based method for recognition of isolated sign considering static and dynamic behaviour of Indian sign language ISL. The proposed methodology consists of three modules: preprocessing, feature extraction and classification. In the preprocessing module, various steps such as skin colour segmentation, redundant frames removal RFR algorithm and face elimination have been performed. The purpose of RFR algorithm is to remove redundant frames from the sign video to speed up the recognition task. In the feature extraction module, multiple features have been extracted. A multi-class support vector machine MSVM and Bayesian K-nearest neighbour BKNN are used to classify the signs. Experimentation with vocabulary of 21 sign from ISL is conducted and the results prove that the proposed method for recognition of gestured sign is effective and having high accuracy. Experimental results demonstrate that the proposed system can recognise signs with 95.3% accuracy.

Suspicious human activity recognition: a review

Suspicious human activity recognition from surveillance video is an active research area of image processing and computer vision. Through the visual surveillance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shopping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very difficult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Recent decade witnessed a good number of publications in the field of visual surveillance to recognize the abnormal activities. Furthermore, a few surveys can be seen in the literature for the different abnormal activities recognition; but none of them have addressed different abnormal activities in a review. In this paper, we present the state-of-the-art which demonstrates the overall progress of suspicious activity recognition from the surveillance videos in the last decade. We include a brief introduction of the suspicious human activity recognition with its issues and challenges. This paper consists of six abnormal activities such as abandoned object detection, theft detection, fall detection, accidents and illegal parking detection on road, violence activity detection, and fire detection. In general, we have discussed all the steps those have been followed to recognize the human activity from the surveillance videos in the literature; such as foreground object extraction, object detection based on tracking or non-tracking methods, feature extraction, classification; activity analysis and recognition. The objective of this paper is to provide the literature review of six different suspicious activity recognition systems with its general framework to the researchers of this field.

A survey on manual and non-manual sign language recognition for isolated and continuous sign

Sign language recognition is an important area of human computer interaction (HCI). The last decade witnessed a good number of publications in this field. Furthermore, several surveys can be found in the literature but none of them addresses an overall review in this field. In this paper, we have specifically highlighted the Indian sign language (ISL). The works under the complex and moving background, integration of non-manual signals, large vocabulary and signer independent have got a very little attention in the past. In this paper, we have discussed hand segmentation and tracking, feature extraction and classification methods exist in the literature. Within these methods, we examine the various issues such as signer dependence/independence, manual/non-manual, glove/device-based, vocabulary size, constraints in hand segmentation, and isolated/continuous sign. The purpose of this paper is to provide a complete progress in the field of SLR, specifically in ISL.

A framework for dynamic hand Gesture Recognition using key frames extraction

Hand Gesture Recognition is one of the natural ways of human computer interaction (HCI) which has wide range of technological as well as social applications. A dynamic hand gesture can be characterized by its shape, position and movement. This paper presents a user independent framework for dynamic hand gesture recognition in which a novel algorithm for extraction of key frames is proposed. This algorithm is based on the change in hand shape and position, to find out the most important and distinguishing frames from the video of the hand gesture, using certain parameters and dynamic threshold. For classification, Multiclass Support Vector Machine (MSVM) is used. Experiments using the videos of hand gestures of Indian Sign Language show the effectiveness of the proposed system for various dynamic hand gestures. The use of key frame extraction algorithm speeds up the system by selecting essential frames and therefore eliminating extra computation on redundant frames.

Abandoned or removed object detection from visual surveillance: a review

Intelligent Visual Surveillance is an important and challenging research field of image processing and computer vision. To prevent the ecological and economical losses from bomb blasting, an intelligent visual surveillance is required to keep an eye on public areas, infrastructures and discriminate an unattended object left among multiple objects at public places. An unattended object without its owner since a long time at public place is considered as an abandoned object. Identification of an abandoned object on real-time can prevent the terrorists attack through an automated video surveillance system. In recent decade, a good number of publications have been presented in the field of intelligent visual surveillance to identify the abandoned or removed objects. Furthermore, few surveys can be seen in the literature for the various human activity recognition but none of them focused deeply on abandoned or removed object detection in a review. In this paper, we present the state-of-the-art which demonstrates the overall progress of abandoned or removed object detection from the surveillance videos in the last decade. We include a brief introduction of the abandoned object detection with its issues and challenges. To acknowledge to the new researchers of this field, core technologies, and frequently used general steps to recognize abandoned or removed objects have been discussed in the literature such as foreground extraction, static object detection based on non-tracking or tracking approaches, feature extraction, classification and activity analysis to recognize abandoned object. The objective of this paper is to provide the literature review in the field of abandoned or removed object recognition from visual surveillance systems with its general framework to the researchers of this field.

A Hybrid Method for Image Categorization Using Shape Descriptors and Histogram of Oriented Gradients

Image categorization is the process of classifying all pixels of an image into one of several classes. In this paper, we have proposed a novel vision-based method for image categorization is invariant to affine transformation and robust to cluttered background. The proposed methodology consists of three phases: segmentation, feature extraction, and classification. In segmentation, an object of interest is segmented from the image. Features representing the image are extracted in feature extraction phase. Finally, an image is classified using multi-class support vector machine. The main advantage of this method is that it is simple and computationally efficient. We have tested the performance of proposed system on Caltech 101 object category and reported 76.14 % recognition accuracy.