Abstract Gesture recognition nowadays has grabbed the attention of researchers as they represent human behaviour in multiple practical ways. Amongst a variety of gestures available, hand gestures play an essential role in the field of human‐computer interaction when recognised efficiently in complex and dynamic environments. In this paper, we propose a dynamic hand gesture recognition system to recognise hand gestures appearing in different indoor and outdoor environments. Hand detection and tracking uses a two‐level system resulting in the formation of gesture trajectory in challenging conditions in which existing detection and tracking algorithms could not do so. A set of 45 features is provided as input to the various classification techniques. The redundancy problem has been reduced by selecting a set of optimum features using the analysis of variance method, which ranks the list of features. An incremental feature selection technique calculates recognition accuracy by selecting features according to rankings. This system provides an accuracy of 96.32% when used with machine learning and 97.5% when used with deep learning techniques. Recognition accuracy is calculated for various environments, including an extra hand, multiple persons in the video frame, and outdoor environment. All machine‐learning classifiers are combined using classifier combination to calculate the accuracy according to the majority‐voting rule. Based on the experimental results, it has been observed that deep learning provides better results compared to machine learning.

In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is proposed for removal of high‐density impulse noise from colour images. In the proposed filtering scheme, the noisy and non‐noisy pixels are classified based on the non‐causal linear prediction error. For a noisy pixel, the adaptive VMF is processed over the pixel where the window size is adapted based on the availability of good pixels. Whereas, a non‐noisy pixel is substituted with the weighted mean of the good pixels of the processing window. The experiments have been carried out on a large database for different classes of images, and the performance is measured in terms of peak signal‐to‐noise ratio, mean squared error, structural similarity and feature similarity index. It is observed from the experiments that the proposed filter outperforms (∼1.5 to 6 dB improvement) some of the existing noise removal techniques not only at low density impulse noise but also at high‐density impulse noise.

Hand gestures are a natural way of communication among humans in everyday life. Presence of spatiotemporal variations and unwanted movements within a gesture called self co‐articulation makes the segmentation a challenging task. The study reveals that the self co‐articulation may be used as one of the feature to enhance the performance of hand gesture recognition system. It was detected from the gesture trajectory by addition of speed information along with the pause in the gesture spotting phase. Moreover, a new set of novel features in the feature extraction stage was used such as position of the hand, self co‐articulated features, ratio and distance features. The ANN and SVM were used to develop two independent models using new set of features as input. The models based on CRF and HCRF was used to develop the baseline system for the present study. The experimental results suggest that the proposed new set of features provides improvement in terms of accuracy using ANN (7.48%) and SVM (9.38%) based models as compared with baseline CRF based model. There are also significant improvements in the performances of both ANN (2.08%) and SVM (3.98%) based models as compared with HCRF based model.