Tracking humans in crowded video sequences is an important constituent of visual scene understanding. Increasing crowd density challenges visibility of humans, limiting the scalability of existing pedestrian trackers to higher crowd densities. For that reason, we propose to revitalize head tracking with Crowd of Heads Dataset (CroHD), consisting of 9 sequences of 11,463 frames with over 2,276,838 heads and 5,230 tracks annotated in diverse scenes. For evaluation, we proposed a new metric, IDEucl, to measure an algorithm's efficacy in preserving a unique identity for the longest stretch in image coordinate space, thus building a correspondence between pedestrian crowd motion and the performance of a tracking algorithm. Moreover, we also propose a new head detector, HeadHunter, which is designed for small head detection in crowded scenes. We extend HeadHunter with a Particle Filter and a color histogram based re-identification module for head tracking. To establish this as a strong baseline, we compare our tracker with existing state-of-the-art pedestrian trackers on CroHD and demonstrate superiority, especially in identity preserving tracking metrics. With a light-weight head detector and a tracker which is efficient at identity preservation, we believe our contributions will serve useful in advancement of pedestrian tracking in dense crowds.

No abstract

We analyse the chaotic motion and its shape dependence in a piecewise linear map using Fujisaka's characteristic function method. The map is a generalization of the one introduced by R. Artuso. Exact expressions for diffusion coefficient are obtained giving previously obtained results as special cases. Fluctuation spectrum relating to probability density function is obtained in a parametric form. We also give limiting forms of the above quantities. Dependence of diffusion coefficient and probability density function on the shape of the map is examined.

No abstract

Growing demand for e-healthcare across the globe has raised concerns towards the secure and authentication enhanced medical image sharing. One of the services offered by health informatics in hospitals include an user interface through the Local Area Network (LAN) for enabling storage and access of medical records. In this paper, a security enhanced DICOM image sharing over a LAN addressing confidentiality, integrity and authentication has been proposed. Initially, the AES encrypted patient history was combined along with the thumb impression and Quick Response (QR) code of patient ID as watermark. This watermark was encrypted employing Integer Wavelet Transform (IWT), chaotic map and attractors with confusion-diffusion operations. Further, the encrypted watermark was embedded in the selected Region Of Non-Interest (RONI) pixels of DICOM image. Username & unique password credentials, Face identification and FPGA generated One Time Password (OTP) form the three layer authentication scheme for secure DICOM image access through the LAN. Web publishing medium of storing secured DICOM images in cloud has also been addressed in this work. To validate the proposed hybrid crypto-watermarking system, parameters such as key sensitivity, key space, correlation, entropy, histogram, cropping attack, Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) were performed and the results obtained have proved the strength of the proposed algorithm against brute force, statistical and cropping attacks.