Interactions with virtual 3D objects in the virtual reality (VR) environment using the gesture of fingers captured in a wearable 2D camera have emerging applications in real-life. This paper presents an approach of a two-stage convolutional neural network, one for the detection of hand and another for the fingertips. One purpose of VR environments is to transform a virtual 3D object with affine parameters by using the gesture of thumb and index fingers. To evaluate the performance of the proposed system, one existing, and another developed egocentric fingertip databases are employed so that learning involves large variations that are common in real-life. Experimental results show that the proposed fingertip detection system outperforms the existing systems in terms of the precision of detection. The interaction performance of the proposed system in the VR environment is higher than that of the existing systems in terms of estimation error and correlation between the ground truth and estimated affine parameters.

This paper presents an approach of two-stage convolutional neural network (CNN) for detection of fingertips so that an interaction of the fingertips with a 3D object in the virtual environment (VR) can be established. The first-stage CNN is assigned to detect and locate the hand. Subsequently, the detected hand is cropped, resized, and fed to the second stage CNN for predicting the coordinates of fingertips. Next, a tracker is employed to track the hand continuously so that the system becomes reliable in real-time performance. The VR environments are designed to demonstrate the performance of the fingertip-based interaction system. The proposed method focuses on the geometric transformation of a virtual 3D object by using the gesture of the thumb and index finger. In particular, the distance of the thumb and index fingertips is employed to scale a 3D object in virtual environment. To realize the system, a dataset of 1000 images, named, Thumb Index 1000 (TI1K) dataset, is developed including those variations that are commonly-seen in real-life thumb and index fingers. The system is evaluated with the aid of a number of participants and virtual objects that are distinctive in nature. The proposed approach attains the desired goal and performs in real-time seamlessly to facilitate the human-computer interaction in the VR environment.

Busy life as well as the prevalence of infotainment is increasingly making people more occupied even during tasks that require serious attention. One such task is driving and at the same time getting involved in activities that may distract drivers cognitively from watching the road and cause fatal accidents. This paper presents a method that is capable of monitoring different types of distractions, such as talking and texting on cell phone, casual eating, and operating cabin equipment while driving, so that a driver can be assisted to remain cautious on the road. The proposed method automatically detects and tracks fiducial body parts of a driver from video captured by a camera mounted on the front windshield inside a vehicle. Relative distances between the tracking trajectories are used as features that represent actions of the driver. Then, the well-known kernel support vector machine is applied for recognizing a particular distraction from the features extracted from body parts. The proposed feature is also compared with previously employed features for tracking-based human action recognition schemes to substantiate its better result in terms of mean accuracy and robustness for distraction recognition. The effectiveness of the proposed method of distraction recognition is also analyzed with respect to tracking errors.

Person re-identification is a well-known technique for recognizing a person, who is continuously observed by nonoverlapping cameras in a wide area surveillance. Body part-based re-identification gives a higher level of importance to certain parts of body instead of giving equal importance to whole body. This is mainly due to the fact that certain body parts can be distinguishable better than the others in presence of variable viewing positions, occlusions, illuminations and resolutions of the scenes. This paper focuses on the evaluation of the discrimination power of three body parts, viz., head, torso, and leg for their potential use in fusion-based re-identification technique. In particular, the transformation of features between two body parts is conducted in a warp function space that consists of positive cost for same pair of targets and negative cost for different pair of targets. The support vector machine-based classifier is used to discriminate these two types of cost and the results are evaluated in terms of correct recognition for a given false alarm rate. Experiments are conducted on two publicly available databases, namely, CAVIAR4ReID and CUHK01. The results conclude that torso is the most important body part in the problem of person re-identification.

Distraction during driving is a growing concern for global road safety. Different activities impertinent to driving hinder the concentration of driver on road and often cause substantial damage to life and property. For making driving safe, an algorithm is proposed in this paper that is capable of detecting distraction during driving. The proposed algorithm tracks key body parts of the driver in video captured by a front camera. Euclidean distances between the tracking trajectories of body parts are used as representative features that characterize the state of distraction or attention of a driver. The well-known K-nearest neighbor classifier is applied for detecting distraction from the features extracted from body parts. The proposed method is compared with existing methods implementing tracking-based human action identification to corroborate its improved performance.

Detection and classification of vehicles are two of the most challenging tasks of a video-based intelligent transportation system. Traditional detection and classification methods are computationally highly expensive and become unsuccessful in many cases such as occlusion among the vehicles and when differences between pixel intensities of vehicles and backgrounds are small. In this paper, a novel detection and classification method is proposed using multiple time-spatial images (TSIs), each obtained from a virtual detection line on the frames of a video. Such a use of multiple TSIs provides the opportunity to identify the latent occlusions among the vehicles and to reduce the dependencies of the pixel intensities between the still and moving objects to increase the accuracy of detection performance as well as to achieve an improved classification performance. In order to identify the class of a particular vehicle, a two-step k nearest neighborhood classification scheme is proposed by utilizing the shape-based, shape-invariant, and texture-based features of the segmented regions corresponding to the vehicle appeared in appropriate frames that are determined from the TSIs of the video. Extensive experimentations are carried out in vehicular traffics of varying environments to evaluate the detection and classification performance of the proposed method, as compared with the existing methods. Experimental results demonstrate that the proposed method provides a significant improvement in counting and classifying the vehicles in terms of accuracy and robustness alongside a substantial reduction of execution time, as compared with that of the other methods.

A new bivariate maximum a posteriori estimator is proposed for the magnitude components of the dual-tree complex wavelet transform (DT-CWT) coefficients in order to reduce additive white Gaussian noise in a video. The estimator considers the fact that the magnitude components of the DT-CWT coefficients of the Gaussian distributed noise fit the generalized Gamma distribution very well. For spatial filtering, the joint distribution function of the magnitude components of the DT-CWT coefficients of the two neighboring frames of a video is considered to be locally-adaptive bivariate Gaussian having a non-negative mean. The correlation coefficient of this distribution function acts as an indirect measure of the motion of the DT-CWT coefficients between two neighboring frames. A recursive time averaging of the spatially filtered magnitude components is adopted for further noise reduction. Experimental results on test video sequences show that the proposed estimator provides an average peak signal-to-noise ratio that is higher than that provided by the others.

Traditional statistical detectors of the discrete wavelet transform (DWT)-based image watermarking use probability density functions (PDFs) that show inadequate matching with the empirical PDF of image coefficients in view of the fact that they use a fixed number of parameters. Hence, the decision values obtained from the estimated thresholds of these detectors provide substandard detection performance. In this paper, a new detector is proposed for the DWT-based additive image watermarking, wherein a PDF based on the Gauss-Hermite expansion is used, in view of the fact that this PDF provides a better statistical match to the empirical PDF by utilizing an appropriate number of parameters estimated from higher-order moments of the image coefficients. The decision threshold and the receiver operating characteristics are derived for the proposed detector. Experimental results on test images demonstrate that the proposed watermark detector performs better than other standard detectors such as the Gaussian and generalized Gaussian (GG), in terms of the probabilities of detection and false alarm as well as the efficacy. It is also shown that detection performance of the proposed detector is more robust than the competitive GG detector in the case of compression, additive white Gaussian noise, filtering, or geometric attack.

A new detector using modified Gauss-Hermite (GH) probability density function (PDF) is proposed for the wavelet-domain image watermarking scheme. It is shown that the proposed PDF matches the empirical one of image wavelet coefficients better than other conventional PDFs such as the generalized Gaussian and Bessel K-form. This is because of the fact that the modified GH PDF utilizes an arbitrary number of higher order moments of the wavelet coefficients instead of considering only the first few for the parameter estimation process. The proposed PDF is then used for designing the statistical detector for a wavelet-based image watermarking algorithm. Experimental results on a standard image database show that the proposed detector provides a higher detection probability and lower false alarm than that provided by the others.

The probability density functions (PDFs) of the wavelet coefficients play a key role in many wavelet-based image processing algorithms, such as denoising. The conventional PDFs usually have a limited number of parameters that are calculated from the first few moments only. Consequently, such PDFs cannot be made to fit very well with the empirical PDF of the wavelet coefficients of an image. As a result, the shrinkage function utilizing any of these density functions provides a substandard denoising performance. In order for the probabilistic model of the image wavelet coefficients to be able to incorporate an appropriate number of parameters that are dependent on the higher order moments, a PDF using a series expansion in terms of the Hermite polynomials that are orthogonal with respect to the standard Gaussian weight function, is introduced. A modification in the series function is introduced so that only a finite number of terms can be used to model the image wavelet coefficients, ensuring at the same time the resulting PDF to be non-negative. It is shown that the proposed PDF matches the empirical one better than some of the standard ones, such as the generalized Gaussian or Bessel K-form PDF. A Bayesian image denoising technique is then proposed, wherein the new PDF is exploited to statistically model the subband as well as the local neighboring image wavelet coefficients. Experimental results on several test images demonstrate that the proposed denoising method, both in the subband-adaptive and locally adaptive conditions, provides a performance better than that of most of the methods that use PDFs with limited number of parameters.

Statistical estimation techniques for the wavelet-based image denoising use suitable probability density functions (PDFs) as prior functions for the image coefficients. Due to the intrascale dependency of the local neighboring image wavelet coefficients, the prior functions are assumed to be stationary. In this paper, it is shown that the stationary Gram-Charlier (GC) PDF models the image coefficients better than the traditional ones, such as the stationary Gaussian and stationary generalized Gaussian PDFs. A Bayesian wavelet-based maximum a posteriori estimator is then developed by using the proposed GC prior function. Experimental results on standard images show that the proposed estimator provides a denoising performance, which is better than that of several existing denoising methods in terms of signal-to-noise ratio and visual quality.

The paper proposes a joint probability density function to model the video wavelet coefficients of any two neighboring frames and then applies this statistical model for denoising. The parameter of the density function that measures the correlation between the wavelet coefficients of the two frames is used as an index for the motion. The joint density function is employed for spatial filtering of the noisy wavelet coefficients by developing a bivariate maximum a posteriori estimator. A recursive time averaging of the spatially filtered wavelet coefficients is adopted for further noise reduction. Simulation results on test video sequences show an improved performance both in terms of the peak signal-to-noise ratio and the perceptual quality compared to that of the other denoising algorithms