Visual cryptography is considered to be a vital technique for hiding visual data from intruders. Because of its importance, it finds applications in various sectors such as E-voting system, financial documents and copyright protections etc. A number of methods have been proposed in past for encrypting color images such as color decomposition, contrast manipulation, polynomial method, using the difference in color intensity values in a color image etc. The major flaws with most of the earlier proposed methods is the complexity encountered during the implementation of the methods on a wide scale basis, the problem of random pixilation and insertion of noise in encrypted images. This paper presents a simple and highly resistant algorithm for visual cryptography to be performed on color images. The main advantage of the proposed cryptographic algorithm is the robustness and low computational cost with structure simplicity. The proposed algorithm outperformed the conventional methods when tested over sample images proven using key analysis, SSIM and histogram analysis tests. In addition, the proposed method overshadows the standard method in terms of the signal to noise ratio obtained for the encrypted image, which is much better than the SNR value obtained using the standard method. The paper also makes a worst case analysis for the SNR values for both the methods.

The important task of human tracking can be difficult to implement in real world environment as the videos can involve complex scenes, severe occlusion and even moving background. Tracking individual objects in a cluttered scene is an important aspect of surveillance. In addition, the systems should also avoid misclassification which can lead to inaccurate tracking. This paper makes use of an efficient image annotation for human tracking. According to the literature survey, this is the first paper which proposes the application of the image annotation algorithm towards human tracking. The method divides the video scene into multiple layers assigning each layer to the individual object of interest. Since each layer has been assigned to a specific object in the video sequence: (i) we can track and analyse the movement of each object individually (ii) The method is able to reframe from misclassification as each object has been assigned a respective layer. The error incurred by the system with movement from one frame to another is presented with detailed simulations and is compared with the conventional Horn–Schunck alone.

Tracking occluded objects at different depths has become as extremely important component of study for any video sequence having wide applications in object tracking, scene recognition, coding, editing the videos and mosaicking. This paper experiments with the capabilities of image annotation contour based tracking for occluded object. Image annotation is applied on 3 similar normal video sequences varying in depth. In the experiment, one bike occludes the other at a depth of 60 cm, 80 cm and 100 cm respectively. The effect on tracking is also analyzed with illumination variations using 3 different light sources in video sequences having objects occluding one another at same depth. The paper finally studies the ability of annotation to track the occluded object based on pyramids with variation in depth further establishing a threshold at which the ability of the system to track the occluded object fails. The contour of both the individual objects can’t be tracked due to the distortion caused by overlapping of the object pyramids. The thresholds established can be used as a bench mark to estimate the capability of different softwares. The paper further computes the frame by frame error incurred by the system, supported by detailed simulations. This system can be effectively used to achieve flawless tracking as the error in motion tracking can be corrected. This can be of great interest to computer scientists while designing surveillance systems etc.